2010 TREC Legal Track: Enron Discovery: responsive classification
bdanalytics
Date: (Mon) Apr 27, 2015
Introduction:
Data: Source: Training: https://courses.edx.org/c4x/MITx/15.071x_2/asset/energy_bids.csv
New:
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- fit.models chunk:
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- move model_type parameter to myfit_mdl before indep_vars_vctr (keep all model_* together)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- Skip trControl.method=“cv” for dummy classifier ?
- Add custom model to caret for a dummy (baseline) classifier (binomial & multinomial) that generates proba/outcomes which mimics the freq distribution of glb_rsp_var values; Right now glb_dmy_glm_mdl always generates most frequent outcome in training data
glm_dmy_mdl should use the same method as glm_sel_mdl until custom dummy classifer is implemented
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Replicate myfit_mdl_classification features in myfit_mdl_regression
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list=ls())
set.seed(12345)
options(stringsAsFactors=FALSE)
source("~/Dropbox/datascience/R/mydsutils.R")
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
# Gather all package requirements here
#suppressPackageStartupMessages(require())
#packageVersion("snow")
#require(sos); findFn("pinv", maxPages=2, sortby="MaxScore")
# Analysis control global variables
glb_trnng_url <- "https://courses.edx.org/c4x/MITx/15.071x_2/asset/energy_bids.csv"
glb_newdt_url <- "<newdt_url>"
glb_is_separate_newent_dataset <- FALSE # or TRUE
glb_split_entity_newent_datasets <- TRUE # or FALSE
glb_split_newdata_method <- "sample" # "condition" or "sample" or "copy"
glb_split_newdata_condition <- "<col_name> <condition_operator> <value>" # or NULL
glb_split_newdata_size_ratio <- 0.3 # > 0 & < 1
glb_split_sample.seed <- 144 # or any integer
glb_max_obs <- NULL # or any integer
glb_is_regression <- FALSE; glb_is_classification <- TRUE; glb_is_binomial <- TRUE
glb_rsp_var_raw <- "responsive"
# for classification, the response variable has to be a factor
glb_rsp_var <- "responsive.fctr" # or glb_rsp_var_raw
# if the response factor is based on numbers e.g (0/1 vs. "A"/"B"),
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- function(raw) {
relevel(factor(ifelse(raw == 1, "Y", "N")), as.factor(c("Y", "N")), ref="N")
#as.factor(paste0("B", raw))
#as.factor(raw)
}
glb_map_rsp_raw_to_var(c(1, 1, 0, 0, 0))## [1] Y Y N N N
## Levels: N Yglb_map_rsp_var_to_raw <- function(var) {
as.numeric(var) - 1
#as.numeric(var)
#levels(var)[as.numeric(var)]
#c(" <=50K", " >50K")[as.numeric(var)]
}
glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(c(1, 1, 0, 0, 0)))## [1] 1 1 0 0 0if ((glb_rsp_var != glb_rsp_var_raw) & is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
glb_rsp_var_out <- paste0(glb_rsp_var, ".predict.") # model_id is appended later
glb_id_vars <- NULL # or c("<id_var>")
glb_is_textual <- TRUE # or FALSE # vs. glb_is_numerical ???
#Sys.setlocale("LC_ALL", "C") # For english
glb_txt_var <- c("email") # or NULL
glb_append_stop_words <- NULL # or c("<freq_word>")
glb_sprs_threshold <- 0.970
# List transformed vars
glb_exclude_vars_as_features <- NULL # or c("<var_name>")
if (glb_is_textual)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_txt_var)
if (glb_rsp_var_raw != glb_rsp_var)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_rsp_var_raw)
# List feats that shd be excluded due to known causation by prediction variable
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(NULL)) # or c("<col_name>")
# List output vars (useful during testing in console)
# glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
# grep(glb_rsp_var_out, names(glb_trnent_df), value=TRUE))
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
# rpart: .rnorm messes with the models badly
# caret creates dummy vars for factor feats which messes up the tuning
# - better to feed as.numeric(<feat>.fctr) to caret
# Regression
if (glb_is_regression)
glb_models_method_vctr <- c("lm", "glm", "rpart", "rf") else
# Classification
if (glb_is_binomial)
glb_models_method_vctr <- c("glm", "rpart", "rf") else
glb_models_method_vctr <- c("rpart", "rf")
glb_models_lst <- list(); glb_models_df <- data.frame()
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<col_name>")
glb_model_metric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glb_model_metric <- NULL # or "<metric_name>"
glb_model_metric_maximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glb_model_metric_smmry <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glb_model_metric_terms)
# metric <- sum(confusion_mtrx * glb_model_metric_terms) / nrow(data)
# names(metric) <- glb_model_metric
# return(metric)
# }
glb_tune_models_df <-
rbind(
#data.frame(parameter="cp", min=0.00005, max=0.00005, by=0.000005),
#seq(from=0.01, to=0.01, by=0.01)
#data.frame(parameter="mtry", min=2, max=4, by=1),
data.frame(parameter="dummy", min=2, max=4, by=1)
)
# or NULL
glb_n_cv_folds <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glb_model_evl_criteria <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glb_model_evl_criteria <- c("max.Accuracy.OOB", "max.Kappa.OOB", "min.aic.fit") else
glb_model_evl_criteria <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
glb_sel_mdl_id <- NULL # or "<model_id_prefix>.<model_method>"
glb_fin_mdl_id <- glb_sel_mdl_id # or "Final"
glb_out_pfx <- "Enron_Discovery_"
# Depict process
glb_analytics_pn <- petrinet(name="glb_analytics_pn",
trans_df=data.frame(id=1:6,
name=c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df=data.frame(
begin=c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end =c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_script_tm <- proc.time()
glb_script_df <- data.frame(chunk_label="import_data",
chunk_step_major=1, chunk_step_minor=0,
elapsed=(proc.time() - glb_script_tm)["elapsed"])
print(tail(glb_script_df, 2))## chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed import_data 1 0 0.002Step 1: import data
glb_entity_df <- myimport_data(url=glb_trnng_url,
comment=ifelse(!glb_is_separate_newent_dataset, "glb_entity_df", "glb_trnent_df"),
force_header=TRUE)## [1] "Reading file ./data/energy_bids.csv..."
## [1] "dimensions of data in ./data/energy_bids.csv: 855 rows x 2 cols"
## email
## 1 North America's integrated electricity market requires cooperation on environmental policies Commission for Environmental Cooperation releases working paper on North America's electricity market Montreal, 27 November 2001 -- The North American Commission for Environmental Cooperation (CEC) is releasing a working paper highlighting the trend towards increasing trade, competition and cross-border investment in electricity between Canada, Mexico and the United States. It is hoped that the working paper, Environmental Challenges and Opportunities in the Evolving North American Electricity Market, will stimulate public discussion around a CEC symposium of the same title about the need to coordinate environmental policies trinationally as a North America-wide electricity market develops. The CEC symposium will take place in San Diego on 29-30 November, and will bring together leading experts from industry, academia, NGOs and the governments of Canada, Mexico and the United States to consider the impact of the evolving continental electricity market on human health and the environment. "Our goal [with the working paper and the symposium] is to highlight key environmental issues that must be addressed as the electricity markets in North America become more and more integrated," said Janine Ferretti, executive director of the CEC. "We want to stimulate discussion around the important policy questions being raised so that countries can cooperate in their approach to energy and the environment." The CEC, an international organization created under an environmental side agreement to NAFTA known as the North American Agreement on Environmental Cooperation, was established to address regional environmental concerns, help prevent potential trade and environmental conflicts, and promote the effective enforcement of environmental law. The CEC Secretariat believes that greater North American cooperation on environmental policies regarding the continental electricity market is necessary to: * protect air quality and mitigate climate change, * minimize the possibility of environment-based trade disputes, * ensure a dependable supply of reasonably priced electricity across North America * avoid creation of pollution havens, and * ensure local and national environmental measures remain effective. The Changing Market The working paper profiles the rapid changing North American electricity market. For example, in 2001, the US is projected to export 13.1 thousand gigawatt-hours (GWh) of electricity to Canada and Mexico. By 2007, this number is projected to grow to 16.9 thousand GWh of electricity. "Over the past few decades, the North American electricity market has developed into a complex array of cross-border transactions and relationships," said Phil Sharp, former US congressman and chairman of the CEC's Electricity Advisory Board. "We need to achieve this new level of cooperation in our environmental approaches as well." The Environmental Profile of the Electricity Sector The electricity sector is the single largest source of nationally reported toxins in the United States and Canada and a large source in Mexico. In the US, the electricity sector emits approximately 25 percent of all NOx emissions, roughly 35 percent of all CO2 emissions, 25 percent of all mercury emissions and almost 70 percent of SO2 emissions. These emissions have a large impact on airsheds, watersheds and migratory species corridors that are often shared between the three North American countries. "We want to discuss the possible outcomes from greater efforts to coordinate federal, state or provincial environmental laws and policies that relate to the electricity sector," said Ferretti. "How can we develop more compatible environmental approaches to help make domestic environmental policies more effective?" The Effects of an Integrated Electricity Market One key issue raised in the paper is the effect of market integration on the competitiveness of particular fuels such as coal, natural gas or renewables. Fuel choice largely determines environmental impacts from a specific facility, along with pollution control technologies, performance standards and regulations. The paper highlights other impacts of a highly competitive market as well. For example, concerns about so called "pollution havens" arise when significant differences in environmental laws or enforcement practices induce power companies to locate their operations in jurisdictions with lower standards. "The CEC Secretariat is exploring what additional environmental policies will work in this restructured market and how these policies can be adapted to ensure that they enhance competitiveness and benefit the entire region," said Sharp. Because trade rules and policy measures directly influence the variables that drive a successfully integrated North American electricity market, the working paper also addresses fuel choice, technology, pollution control strategies and subsidies. The CEC will use the information gathered during the discussion period to develop a final report that will be submitted to the Council in early 2002. For more information or to view the live video webcast of the symposium, please go to: http://www.cec.org/electricity. You may download the working paper and other supporting documents from: http://www.cec.org/programs_projects/other_initiatives/electricity/docs.cfm?varlan=english. Commission for Environmental Cooperation 393, rue St-Jacques Ouest, Bureau 200 Montréal (Québec) Canada H2Y 1N9 Tel: (514) 350-4300; Fax: (514) 350-4314 E-mail: info@ccemtl.org ***********
## 2 FYI -----Original Message----- From: \t"Ginny Feliciano" <gfeliciano@earthlink.net>@ENRON [mailto:IMCEANOTES-+22Ginny+20Feliciano+22+20+3Cgfeliciano+40earthlink+2Enet+3E+40ENRON@ENRON.com] Sent:\tThursday, June 28, 2001 3:40 PM To:\tSilvia Woodard; Paul Runci; Katrin Thomas; John A. Riggs; Kurt E. Yeager; Gregg Ward; Philip K. Verleger; Admiral Richard H. Truly; Susan Tomasky; Tsutomu Toichi; Susan F. Tierney; John A. Strom; Gerald M. Stokes; Kevin Stoffer; Edward M. Stern; Irwin M. Stelzer; Hoff Stauffer; Steven R. Spencer; Robert Smart; Bernie Schroeder; George A. Schreiber, Jr.; Robert N. Schock; James R. Schlesinger; Roger W. Sant; John W. Rowe; James E. Rogers; John F. Riordan; James Ragland; Frank J. Puzio; Tony Prophet; Robert Priddle; Michael Price; John B. Phillips; Robert Perciasepe; D. Louis Peoples; Robert Nordhaus; Walker Nolan; William A. Nitze; Kazutoshi Muramatsu; Ernest J. Moniz; Nancy C. Mohn; Callum McCarthy; Thomas R. Mason; Edward P. Martin; Jan W. Mares; James K. Malernee; S. David Freeman; Edwin Lupberger; Amory B. Lovins; Lynn LeMaster; Hoesung Lee; Lay, Kenneth; Lester Lave; Wilfrid L. Kohl; Soo Kyung Kim; Melanie Kenderdine; Paul L. Joskow; Ira H. Jolles; Frederick E. John; John Jimison; William W. Hogan; Robert A. Hefner, III; James K. Gray; Craig G. Goodman; Charles F. Goff, Jr.; Jerry D. Geist; Fritz Gautschi; Larry G. Garberding; Roger Gale; William Fulkerson; Stephen E. Frank; George Frampton; Juan Eibenschutz; Theodore R. Eck; Congressman John Dingell; Brian N. Dickie; William E. Dickenson; Etienne Deffarges; Wilfried Czernie; Loren C. Cox; Anne Cleary; Bernard H. Cherry; Red Cavaney; Ralph Cavanagh; Thomas R. Casten; Peter Bradford; Peter D. Blair; Ellen Berman; Roger A. Berliner; Michael L. Beatty; Vicky A. Bailey; Merribel S. Ayres; Catherine G. Abbott Subject:\tEnergy Deregulation - California State Auditor Report Attached is my report prepared on behalf of the California State Auditor. I look forward to seeing you at The Aspen Institute Energy Policy Forum. Charles J. Cicchetti Pacific Economics Group, LLC - ca report new.pdf ***********
## 3 14:13:53 Synchronizing Mailbox 'Kean, Steven J.' 14:13:53 Synchronizing Hierarchy 14:13:53 Synchronizing Favorites 14:13:53 Synchronizing Folder 'Inbox' 14:14:04 \t 23 item(s) added to offline folder 14:14:04 \t 1 item(s) changed read-state in offline folder 14:14:04 Synchronizing Folder 'Outbox' 14:14:04 Synchronizing Folder 'Sent Items' 14:14:05 \t 1 item(s) added to offline folder 14:14:05 Synchronizing Folder 'humor' 14:14:05 Synchronizing Folder 'Advertising' 14:14:05 Synchronizing Folder 'HR-data' 14:14:05 Synchronizing Folder 'performance management' 14:14:05 Synchronizing Folder 'federal legislation' 14:14:05 Synchronizing Folder 'Europe' 14:14:05 Synchronizing Folder 'Notes' 14:14:05 Synchronizing Folder 'India' 14:14:06 Synchronizing Folder 'Rice' 14:14:06 Synchronizing Folder 'Pipeline issues' 14:14:06 Synchronizing Folder 'EES' 14:14:06 Synchronizing Folder 'Enron Mentions' 14:14:06 Synchronizing Folder 'Active Markets List' 14:14:06 Synchronizing Folder 'Enrononline' 14:14:06 Synchronizing Folder 'HR stats' 14:14:06 Synchronizing Folder 'OPIC' 14:14:06 Synchronizing Folder 'market structure' 14:14:07 Synchronizing Folder 'aviation' 14:14:07 Synchronizing Folder 'Azurix' 14:14:07 Synchronizing Folder 'PRC' 14:14:07 Synchronizing Folder 'HR' 14:14:07 Synchronizing Folder 'security' 14:14:07 Synchronizing Folder 'internal communications' 14:14:07 Synchronizing Folder 'organizations and associations' 14:14:07 Synchronizing Folder 'press' 14:14:07 Synchronizing Folder 'Press Releases' 14:14:07 Synchronizing Folder 'Project California' 14:14:07 Synchronizing Folder 'Mexico' 14:14:08 Synchronizing Folder 'Brazil' 14:14:08 Synchronizing Folder 'Budget' 14:14:08 Synchronizing Folder 'internet sites' 14:14:08 Synchronizing Folder 'Project Summer' 14:14:08 Synchronizing Folder 'SEC' 14:14:08 Synchronizing Folder 'Environmental Issues' 14:14:08 Synchronizing Folder 'presentations' 14:14:08 Synchronizing Folder 'Calendar' 14:14:08 \t 1 item(s) updated in offline folder 14:14:08 Synchronizing Folder 'California' 14:14:08 Synchronizing Folder 'organizations and phone lists' 14:14:08 Synchronizing Folder 'california - demand buydown' 14:14:08 Synchronizing Folder 'california - emergency authority' 14:14:08 Synchronizing Folder 'California - excess profits tax' 14:14:09 Synchronizing Folder 'California - working group' 14:14:09 Synchronizing Folder 'California campaign' 14:14:09 Synchronizing Folder 'facilities services' 14:14:09 Synchronizing Folder 'California-PG&E bankruptcy' 14:14:09 Synchronizing Folder 'Caliornia - investigations' 14:14:09 Synchronizing Folder 'Enron Wind' 14:14:09 Synchronizing Folder 'campaign 2000' 14:14:09 Synchronizing Folder 'Canada' 14:14:13 Synchronizing Folder 'Iowa' 14:14:13 Synchronizing Folder 'Reg risk' 14:14:13 Synchronizing Folder 'Metals' 14:14:13 Synchronizing Folder 'enron credit' 14:14:13 Synchronizing Folder 'CFTC' 14:14:13 Synchronizing Folder 'PGE' 14:14:13 Synchronizing Folder 'Japan' 14:14:13 Synchronizing Folder 'Coal' 14:14:13 Synchronizing Folder 'florida' 14:14:13 Synchronizing Folder 'Journal' 14:14:14 Synchronizing Folder 'FERC' 14:14:14 Synchronizing Folder 'To Do' 14:14:14 Synchronizing Folder 'Contact list' 14:14:14 Synchronizing Folder 'Contacts' 14:14:14 Synchronizing Folder 'contributions' 14:14:14 Synchronizing Folder 'corporate communications' 14:14:14 Synchronizing Folder 'Corporate responsibility' 14:14:14 Synchronizing Folder 'organization' 14:14:14 Synchronizing Folder 'thank yous' 14:14:14 Synchronizing Folder 'PAC' 14:14:15 Synchronizing Folder 'telecom' 14:14:15 Synchronizing Folder 'OFCCP audit' 14:14:15 Synchronizing Folder 'Tax issues' 14:14:15 Synchronizing Folder 'Tasks' 14:14:15 Synchronizing Folder 'PR-Crisis Management' 14:14:15 Synchronizing Folder 'Strategy and objectives' 14:14:15 Synchronizing Folder 'RTO' 14:14:15 Synchronizing Folder 'Enron global markets' 14:14:15 Synchronizing Folder 'personnel' 14:14:15 Synchronizing Folder 'Enron Advisonry Council' 14:14:15 Synchronizing Folder 'staff meeting' 14:14:15 Synchronizing Folder 'Speeches' 14:14:15 Synchronizing Folder 'Gas' 14:14:15 Synchronizing Folder 'personal' 14:14:15 Synchronizing Folder 'Drafts' 14:14:16 Synchronizing Views 14:14:16 Synchronizing Forms 14:14:40 Done ***********
## 4 ^ ----- Forwarded by Steven J Kean/NA/Enron on 03/02/2001 12:27 PM ----- Suzanne_Nimocks@mckinsey.com Sent by: Carol_Benter@mckinsey.com 03/02/2001 12:04 PM \t To: skean@enron.com cc: Subject: California Power Markets Sorry that we haven't talked in some time. I thought that you would want to take a look at some analysis we have recently completed with regard to the California Power Crisis. You may find some of the analysis to be helpful. Let me know if you have any questions. (See attached file: 10209 zxd414.ppt) +-------------------------------------------------------------+ ***********
## 5 ----- Forwarded by Steven J Kean/NA/Enron on 09/26/2000 04:20 PM ----- "Duncan Kincheloe" <dkincheloe@mpua.org> 09/26/2000 10:22 AM \t To: <skean@enron.com> cc: Subject: Fw: Tagging issue at NERC Security Subcommittee tomorrow Steve, I sent the below to you this morning at an apparently outdated email address. Your secretary was kind enough to update me so I'm forwarding it again. Duncan Kincheloe General Manager and CEO Missouri Public Utility Alliance 573-445-3279 Fax 573-445-0680 ----- Original Message ----- From: Duncan Kincheloe To: Steven Kean Sent: Tuesday, September 26, 2000 10:01 AM Subject: Tagging issue at NERC Security Subcommittee tomorrow Steve, I haven't been in touch since the DOE Reliability Task Force and hope this email address through Elizabeth is still the way to reach you. Incidentally, I was with Sue Tierney at a NARUC conference yesterday and we've started a new pool to wager when Congress will get this issue dealt with. Want in? Perhaps you remember that while we were serving on the Task Force I moved to EPRI to direct their government relations. Last year I moved back to Missouri to run the municipals' state joint action agency, which is why I'm writing. Part of our activity is operating a 19 city power pool (MoPEP) that has a three year supply contract of up to 40 mWh/hr with Enron. We've had some difficulties with delivery since a change in tagging was made in June. The issue and a proposed solution is explained in the attached three page "bullet statement" that we've just submitted to the new NERC Security Subcommittee that meets tomorrow. Based on his discussions with NERC staff, with a Mike Curry with Enron and other affected utilities, my chief operating officer for MoPEP is optimistic about prospects for this resolution. I would like to make sure of Enron's support. I'm told that Dick Ingersoll will be at the subcommittee meeting for Enron, and I know Dick from SPP meetings when I was on the Missouri Commission but don't have contact information on him now. I'd be very grateful if you could pass along a nod of benign interest in this. We'd greatly appreciate Enron's support for the proposed resolution of the tagging issue at the NERC Subcommittee tomorrow, mainly because it should avoid what might otherwise be a messy contract dispute, at FERC or elsewhere, between our organizations. Without getting into the details, my understanding of the issue under the energy purchase agreement has to do with whether the change in NERC tagging that was required on June 1 (the date when MoPEP started taking certain control area services from Western Resources) affects Enron's obligation under the agreement to deliver energy to the Associated Electric Cooperative transmission system. MoPEP's view is that Enron's obligations are unaffected, but some at Enron apparently have had a different view. Our belief is that the proposal that will be presented to the Security Subcommittee will resolve the tagging matter such that both Enron and MoPEP would regard any potential dispute on this issue as moot. Sorry for such short notice, but this resolution and taking it to the subcommittee came out of discussions in Atlanta Friday. Thanks for your consideration, Steve. Hope all is well with you. Duncan Kincheloe General Manager and CEO Missouri Public Utility Alliance 573-445-3279 Fax 573-445-0680 - MoPEP Tagging.doc ***********
## 6 George "Herbert Hoover" Bush. "Robert Kean" <rkean@starband.net> on 07/07/2001 10:13:19 AM Please respond to <rkean@starband.net> To:\t"Alan Kean \\(E-mail\\)" <rex04@msn.com>, "'Doug & Karen Reiman \\(E-mail\\)'" <dkreiman@mcleodusa.net>, "Kathy Wedig \\(E-mail\\)" <kat.wedig@netzero.net>, "Melissa Kean \\(E-mail\\)" <kean@rice.edu>, "Phil Kean \\(E-mail\\)" <kean.philip@mcleodusa.net>, "'Steve & Melissa Kean \\(E-mail\\)'" <skean@enron.com> cc:\t"Diane Kean \\(E-mail 2\\)" <dkean@starband.net> Subject:\tFW: Dadisms Dadisms 1. Run for the roundhouse Nellie, he'll never corner you there. 2. Slick as catshit and twice as nasty. 3. Like shit through a goose. 4. Yeah, that's right, keep fiddlin' with that, you'll break it 5. Ho! Ho! she cried in excess wild and waved her wooden leg on high! 6. On accounta the war. 7. They'll have to shoot him on judgement day. 8. Then of course, there are the names: Katy, Devie, My Little Mochawk, Phil Jofus, and Bubba 9. Choke ass (referring to peanut butter) 10. If it tastes good there are no calories 11. Didn't know you were in town (as driver passes on the right, etc.) 12. Like a couple of cub bears (meaning Rob and Phil) 13. Washington D.C. District or Corruption 14. Have some onions for defense 15. He won't have the guts to do THAT again (when a bug hits the windshield) 16. Saps goin' up the tree 17. Big Al 18. Douglas Doorite 19. Must be a storm comin'; the kids are actin' up 20. Constipate (concentrate) 21. Like being in hell with your back broke 22. gourd cover (hat) 23. mitts, dogs, paws (hands or feet) 24. you're too young 25. Worthington (when we asked him where something was or where he was going) 26. overhose (blue jeans) 27. excruciating pain! (when we squeezed his hand) 28. That's a hell of a note 29. meat wagon (ambulance) 30. drappies (drapes) 31. grappies (grapes) 32. Whoa horsie! Just cuz you only got 3 legs. (when he was putting on the brakes in the car) 33. Jeana, Jeana corpastina, I'm in love with you 34. I love you anyway 35. Scuse my boardin' house reach. 36. Come on out and let me lay a wheel on ya! (in traffic) 37. Sit down and start passin' (at meal time) 38. That's a bit of all right. 39. Here, guard this with your life (when he handed something to us at a store) 40. SOL (shit out of luck) 41. I'll tell ya when your're 21. 42. fernilla, dawberry, shocoletta (vanilla, strawberry, chocolate) 43. straighten up, now. 44. Pardon me soldier. (when he burped) 45. I'm going to assume the position of a soldier. (when he was going to lay on the couch for a nap) 46. horses doovers 47. lemon meringoo pie 48. solft 49. go ahead, see if it bends (when someone pulled out in front of him) 50. Ioway State 51. Cedar Rapids, Cedar Rapids, come on down. Hotel Roosevelt center of town. 52. A hundred dogs going wild in the tobacco field. Does your cigarette taste difference? (a quote from the one Russian guy in Cascade) 53. Cut her loose and let her wander. 54. Get your dogs under the table. 55. "Shit!" cried the queen and a thousand loyal subjects strained their bowels. 56. Who's side are you on?! The power company? (when we left lights on) 57. That makes it soggy and hard to light 58. Full as a tick 59. A treat instead of a treatment 60. Intermittent Power ***********
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## 30 John, With regard to your first question, yes there are additional design capacities to be considered. The Topock lateral, which consists of the three Topock points listed, has a capacity of 400 MDth/d. Additionally, the mainline capacity going west, which serves ALL of our markets in Arizona as well as California, is at 1090 MDth/d. As for your second question, the data fields that were highlighted in red were more for my reference since these required some manual review/correction on my part. If you need further information or explanation, please feel free to contact me. Thanks, Elizabeth John Morris <morris.j@ei.com> on 12/19/2000 02:15:56 PM To: Elizabeth Brown <Elizabeth.Brown@enron.com> cc: Subject: Data Privileged & Confidential Prepared for Counsel Elizabeth, thank you for responding so quickly. I have two questions. 1) You have total capacity of 1550 MDth/d into California. Can TW simultaneously supply all these amounts, or is there some lower limit for total deliveries to all 4 meters? 2) Some of the "Type" cells are colored red. Does the red have any meaning? Thank you, --JRM -- John R. Morris Vice President Economists Incorporated 1200 New Hampshire Avenue, NW Suite 400 Washington, DC 20036 Voice: 202.223.4700 Fax: 202.296.7138 *************************************************** This e-mail, and any attachments thereto, is intended only for use by the addressee(s) named herein and may contain legally privileged and/or confidential information. If you are not the intended recipient of this e-mail, you are hereby notified that any dissemination,distribution or copying of this e-mail, and any attachments thereto, is strictly prohibited. If you have received this e-mail in error, please immediately notify me at (202) 223-4700 and permanently delete the original and any copy of any e-mail and any printout thereof. *************************************************** <] ***********
## 130 ^---------------------- Forwarded by Elizabeth Sager/HOU/ECT on 01/24/2000 03:38 PM --------------------------- "Andy Katz" <AKatz@eei.org> on 01/24/2000 02:26:43 PM To: fmdutton@aep.com, HEMU@dynegy.com, Elizabeth Sager/HOU/ECT@ECT, mroger3@entergy.com, wfhenze@jonesday.com, kdleitao@llgm.com, rosteen@powersrc.com, MPhilips@pwrteam.com, drusso@reliantenergy.com cc: Subject: Fwd: Option B Into Product Just received the Option B Into Product. Marji is available for a conference call tommorow at 10:30 am est. to discuss, but unless I hear objection their definition will go to the working group today along w/ option A. Andrew S. Katz, Senior Attorney Edison Electric Institute 701 Pennsylvania Avenue, N.W. Washington, D.C. 20004 Voice: 202-508-5616 Fax: 202-508-5673 e-mail: akatz@eei.org Received: from pwrteam.com by mail.eei.org; Mon, 24 Jan 2000 15:12:22 -0500 Message-ID: <OF92F70AD3.C81863B0-ON85256870.00687914@com> To: akatz@eei.com Cc: hemu@dynegy.com Subject: Document From: MPhilips@pwrteam.com Date: Mon, 24 Jan 2000 15:10:28 -0500 X-MIMETrack: Serialize by Router on PTN02/Power Team(Release 5.0.2b ***********
## 278 Return-Path: <dtallam@numerix.com> Received: from rly-yc04.mx.aol.com (rly-yc04.mail.aol.com [172.18.149.36]) by air-yc01.mail.aol.com (v77_r1.36) with ESMTP; Fri, 06 Apr 2001 16:23:00 -0500 Received: from nx.numerix.com (nx.numerix.com [63.71.167.197]) by rly-yc04.mx.aol.com (v77_r1.36) with ESMTP; Fri, 06 Apr 2001 16:21:59 -0400 Received: from nx.numerix.com (localhost [127.0.0.1]) by nx.numerix.com (8.9.3/8.9.3) with ESMTP id QAA02498 for <vkaminski@aol.com>; Fri, 6 Apr 2001 16:21:56 -0400 Received: from dtallam (hercules.numerix.com [63.71.167.196]) by nx.numerix.com (8.9.3/8.9.3) with SMTP id QAA02492 for <vkaminski@aol.com>; Fri, 6 Apr 2001 16:21:48 -0400 From: "Dean Tallam" <dtallam@numerix.com> To: <vkaminski@aol.com> Subject: NumeriX Solutions for Enron Date: Fri, 6 Apr 2001 16:16:40 -0400 Message-ID: <001001c0bed6$7e7d6600$da1ea8c0@dtallam> MIME-Version: 1.0 Content-Type: multipart/mixed; boundary="----=_NextPart_000_0011_01C0BEB4.F76BC600" X-Priority: 3 (Normal) X-MSMail-Priority: Normal X-Mailer: Microsoft Outlook CWS, Build 9.0.2416 (9.0.2911.0) Importance: Normal X-MimeOLE: Produced By Microsoft MimeOLE V5.50.4133.2400 Vincent, ˜ It was good speaking with you this afternoon. ˜ NumeriX provides versatile solutions for the pricing and risk management of complex derivative instruments. Of particular interest to Enron may be the use of NumeriX Tools for supporting energy derivatives. ˜ The energy solution provided is a versatile Excel/C/C++ environment for structuring and pricing complex derivatives in the electricity marketplace. The model is based on a microeconomic determination of one or multiple electricity forward curves. Power demands by utility companies are balanced with power generating plants'˜production to determine the marginal price of electricity for multiple geographic regions. Inter-regional transmission constraints are imposed to calculate monthly forward prices for border stations. ˜ The˜energy solution is based on Monte Carlo simulation. The regional power demands and fossil fuel prices are modeled as random processes where model parameters are determined from historical data. Marginal prices from different regions are used to determine border prices. This procedure is iterated over many scenarios and subsequent averages are taken to determine the forward COB prices. ˜ I. Overview NumeriX is a leading provider of˜modular analytical toolkits and off-the-shelf applications that are used for structuring,˜pricing and˜quantifying the risk˜of any˜type of complex˜financial instrument˜or exotic derivative.˜NumeriX solutions are designed for easy integration with existing in-house or third-party trading or risk management applications. NumeriX solutions include: NumeriX Tools NumeriX FI NumeriX FX NumeriX EQ Some of the key benefits of NumeriX solutions include: Support for all asset classes Fixed Income Instruments Equity Instruments Foreign Exchange Instruments Commodity Derivatives Credit Derivatives Insurance Products Ability to value any complex˜financial instrument˜or exotic derivative Increased instrument-valuation precision Leading-edge valuation models and calibration routines Enhanced risk management solutions Stable development platform for building pricing and riskmanagement solutions˜ II. NumeriX Analytics ˜ NumeriX˜provides comprehensive solutions˜that feature advanced tree, simulation (Monte Carlo), stochastic mesh and calibration methods for the following models: One-factor models: Hull-White/Extended Vasicek Black-Karasinski Black-Derman-Toy Black-Scholes Multi-factor models: Multi-factor Hull-White Multi-factor Black-Karasinski Brace-Gatarek-Musiela/Jamshidian NumeriX provides a Monte Carlo methodology that employs leading-edge technology with the following features/benefits: Increased accuracy and faster convergence Better sampling of tail events Adaptive number generation Points do not cluster or leave voids Support for correlated sequences Generates normal and lognormal sequential processes or concurrent˜distributions with optional continuous or discrete bounds Supports public domain and NumeriX developed sequence generators III. NumeriX Tools ˜ NumeriX Tools is a suite of innovative development tools that provide speed, flexibility and ease of use for all aspects of derivatives pricing and risk management, including building new instruments and new models. A collection of object-oriented modules accessed through a well-designed interface available in C or C++, it includes core mathematical and computational functions required for complex derivatives pricing and risk management.˜ NumeriX Tools supports all financial asset classes and is designed to maximize flexibility and minimize development and computation time, each component of the NumeriX Tools integrates many cutting-edge algorithms into one intelligent, easy-to-use API. ˜ IV. NumeriX Applications ˜ NumeriX FI, designed for traders and risk managers, is a comprehensive and flexible off-the-shelf application for structuring, pricing, and quantifying the risk of any type of complex fixed-income instrument, interest-rate derivative, or exotic structure. NumeriX FI offers flexible and robust instrument-definition and structuring capabilities with advanced tree, simulation (Monte Carlo), stochastic-mesh and calibration methods for both single-factor and multi-factor pricing models. ˜ NumeriX FX provides valuation and risk management for a wide collection of plain and exotic foreign exchange options. NumeriX FX includes a variety of Black-Scholes pricing models with a choice of tree- or Monte Carlo-based simulation methodologies. ˜ NumeriX EQ provides valuation and risk management for a wide collection of exotic equity derivatives including barriers, basket options, chooser structures and convertible bonds. Please note that NumeriX EQ is in development and scheduled for release in Q3 of 2001. ˜ NumeriX CR provides valuation and risk management for a wide collection of exotic credit derivatives including credit swaps, asset swaps, total return swaps, spread options, credit-linked notes and credit basket structures. Please note that NumeriX CR is in development and scheduled for release in the later half of 2001. ˜ NumeriX solutions are˜platform independent (Windows NT or Unix) and provided in˜the form of C and C++ libraries˜that are available either as˜a modular toolkit,˜designed to minimize˜the development time for˜structuring new products or models, or˜as an application solution. NumeriX application solutions are available as an Excel application or as˜DLLs designed for quick and easy integration with any third-party trading, risk management or straight-through processing trade support application. ˜ Attached you will find some additional materials describing specific NumeriX solutions. ˜ I look forward to speaking with you on Monday April 9, 2001 at 1:30 your time (2:30 PM NY time). ˜˜˜ If you have any questions please feel free to give me a call at (212) 302-6225. ˜ Sincerely, ˜ ˜ Dean Tallam ˜ - NumeriX Tools-Detailed 2001.pdf - NumeriX FI 2001.pdf ***********
## 435 Eric,please print one copy of each thanks Eric Gillaspie 03/16/2001 03:47 PM To: Barbara N Gray/HOU/ECT@ECT cc: Gerald Nemec/HOU/ECT@ECT, Ann Elizabeth White/HOU/ECT@ECT Subject: DOJ Specification 3 request Barbara, Pursuant to your request for certain form contracts under Specification 3, I have attached the following agreements: Transportation Contracts (Interruptible & Firm) Storage Enron Master Spot Sales Enfolio Master Firm Gas Purchase/Sale Agreement GISB Spot Sales Thanks, Eric Gillaspie 713-345-7667 Enron Building 3886 ***********
## 621 When El Paso gets exercised for replacement reserves by the CAISO and they play the 10 minute ramping games. Don't call EPE to ramp down their generation, if possible run an imbalance and receive the dec prices. I have cleared this with EPE and John Forney. If something happens to change this situation drop me a line. Paul ***********
## 844 Will send right away! Mark Schroeder@ECT 01/26/2001 07:07 AM To: Rosalee Fleming/Corp/Enron@ENRON cc: John Sherriff/LON/ECT@ECT, Mark Frevert/NA/Enron@Enron, Paul Hennemeyer/LON/ECT@ECT, Thomas Schmitz/FRA/ECT@ECT, Carsten Haack/FRA/ECT@ECT, Steven J Kean/NA/Enron@Enron Subject: can you get this to Ken Lay in Davos Rosalee - attached are some talking points prepared by our German gas origination team. They would like to have these forwarded to Ken in Davos, where the person we would like Ken to speak with is also in attendance. That is Mr. Ulf Boge, the President of the Federal Cartel Office (FCO) of Germany. The FCO is about to dismiss a complaint we filed, on rather specious procedural grounds. We regard this as an important case, and if Ken could have word with Boge about how important we regard this case, it would help. thanks mcs ***********
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## 850 I am resending an email that I sent earlier but was returned. ----- Forwarded by Mark Crowther/AP/Enron on 03/27/2001 05:25 PM ----- Mark Crowther 03/27/2001 05:07 PM To: Steven J Kean/HOU/EES@EES, James D Steffes/NA/Enron@Enron, Steve Walton/HOU/ECT@ECT, Stephen D Burns/Corp/Enron@ENRON, Tom Briggs/NA/Enron@Enron, Richard Shapiro/NA/Enron@Enron, Mark Schroeder/LON/ECT@ECT, Paul Dawson/Govt. Affairs/LON/ECT@ECT, Peter Styles/LON/ECT@ECT cc: Nicholas O'Day/AP/Enron@Enron Subject: Brattle Group report on Japanese Electricity Market Gentlemen, In relation to the planned visit to London, Washington DC and Houston by Nick O'Day and myself over the next week to discuss regulatory issues in Japan and our plan for a competitive Japanese market, please find attached the latest draft of the report produced by the Brattle Group. We are very interested in hearing your comments on the ideas in this paper and how they relate to Enron's experience and policy in general. Best regards Mark Crowther Public Affairs Enron Japan ***********
## 851 Steve, The Secretary of Energy Ernesto Martens used to be the CEO of VITRO, our client in for the Monterrey Project. He was a board member until the day of his appointment. He knows Enron well. He used to be the President and CEO of CINTRA the holding company for Mexicana and Aeromexico airlines. When I worked at McKinsey I was engaged in projects with CINTRA for two years. Martens has a reputation of being a good and tough manager. He delivers. On the other hand he also has a reputation of being stubborn and not a good negotiator. We still have to convince him of the proposed electric sector reorganization. The good news is that Fox used many of the phrases that we wrote in the documents for the transition team. Apparently he is following the script. When he talks about no privatization he limits it by saying: there will be no sell out of CFE's current assets and by ensuring that the sector will be open to competition. Dionisio P,rez- Jacome was appointed President of the FERC. He is a classmate of Steffes from the Kennedy School and a good friend of mine. All of the team supporting him is Luis Tellez's team. The undersecretaries, the president of the CRE (FERC) the directors of CFE and LFC. The only new face is the Director of PEMEX who used to be the President and CEO of DuPont in Mexico. Raœl Mu\017oz was very helpful in promoting electric deregulation. About a year ago he organizad a trip to Altamira in the state of Tamaulipas in which he invited members of Congress to visit the DuPont and the BASF plants there so they could see the size of the investments and what was at risk if deregulation did not take place. Jaime Alatorre and I were the only non Congress, non-DuPont or BASF people invited. He and Alatorre are very very good friends. I spent some time with him yesterday at a conference in which I presented a document on energy and economic development and he invited me to his office to discuss competition in natural gas. CFE and LFC have the same directors which may make change more difficult btu it is inevitable. Best, Ricardo Steven J Kean 12/04/2000 04:11 PM To: Ricardo Charvel/NA/Enron@Enron cc: Subject: Energy Analysis How are the appointments turning out from your perspective? ----- Forwarded by Steven J Kean/NA/Enron on 12/04/2000 04:11 PM ----- "Nike Papadopoulos" <nikep@edelmanmexico.com.mx> 12/04/2000 11:22 AM Please respond to nikep To: <max.yzaguirre@enron.com>, <skean@enron.com>, <rshapiro@enron.com>, <eric.thode@enron.com>, <jaime.alatorre@enron.com>, <Ricardo.Charvel@enron.com>, <jaime.williams@enron.com>, <sirvin@enron.com>, <msabine@enron.com>, <agustin.perez@enron.com> cc: <aventura@edelmanmexico.com.mx> Subject: Energy Analysis Attached you will find last week's energy analysis which includes information about Vicente Fox's Energy team. Thank you. - 24Nov-3Dec.DOC ***********
## 852 Congrats!! Ray Alvarez 03/22/2001 04:29 PM To: Richard Shapiro/NA/Enron@Enron cc: Subject: Supreme Decree Rick, I heard from my Bolivia regulatory team that the supreme decree is a reality and that it is very positive- including full recovery of the $100MM gas deferred account that we were hanging out on. I will provide more details as soon as I receive them. Ray ***********
## 853 ----- Forwarded by Steven J Kean/NA/Enron on 03/19/2001 07:50 AM ----- Christie Patrick@ECT 03/15/2001 10:02 AM To: Alice Weekley/ENRON_DEVELOPMENT@ENRON_DEVELOPMENt, Jordan Hunter/FGT/Enron@ENRON, Stephen Veatch/Enron@EnronXGate, Frank S Wang/Corp/Enron@Enron, ricardo_n_calvo@urscorp, roger_w_gunther@ursc cc: Steven J Kean/NA/Enron@Enron, Danny McCarty/ET&S/Enron@Enron, Mike McConnell/HOU/ECT@ECT, Kelly Kimberly/Enron Communications@Enron Communications, Lauren Iannarone/NY/ECT@ECT, Mark Palmer/Corp/Enron@ENRON Subject: Calypso Tribal Letters Friends, The statute should be reviewed by whichever lawyer is supporting the project. As I mentioned to Alice Weekley, I am unfamiliar with the recent amendments referenced requiring the notice for projects that do not actually cross reservation property. The amendment does not surprise me though. In the past, for example, during the Transwestern expansion in '91, we developed a comprehensive NAGPRA plan with tribes whose reservations we crossed, yet neighboring tribes were included--the thinking by the various State Historic Preservation officers was that unearthed remains (bones, funerial objects, pottery pieces, holy objects, etc) may have belonged to tribes other than those from whose reservation they were ultimately unearthed. It made for a VERY VERY long and complicated plan and consultation process....not only when objects were unearthed, but simply putting the required anticipatory plan and procedures in place PRIOR to construction. (It gives me a chuckle: my late husband, Leonard Hilton, who was in charge of the TW project, is likely rolling in his own grave at the mere thought of Enron having to put another of these plans together..Haha!!) As I said, I've not read the amendment , but hopefully it only legally requires 'notice'---yet, in my nearly 20 years of practicing law, I've rarely seen a 'notice' that didn't crack the door or sound the alarm of further buerocratic opportunity! For your further information, I have met Billie Ray Cypress personally and he's a decent and seemingly reasonable guy with a huge casino on the rez ; yet these personal attributes frequently go by the wayside when it comes to 'perceived economic opportunity'. Chief James Billie,[chief of the Seminoles, 2nd largest/2nd wealthiest tribe in North America] on the other hand, is shrewd in business, dictitorial in management, and personally , has an ego bigger than Texas, behavior to match, and is proud of it!! --which makes him a ton of fun to be with if he likes you--he wrestles alligators, writes rock and roll and "plays"in whatever stage opportunity is presented him--as long as he can be the center of attention. The Seminoles have not only a tribal web site, James Billie has his own!! Check it out--it's a hoot!! He has been chief for about 25 years (and I'm guessing he's maybe in his early 50's). The Seminoles have recognized significant economic development under James, but he is definitely a "My way or the highway" kind of guy--he loves you, hates you, or ignores you--and he has a tribal council that is respectfully terrified of him. If any activity smacks of anything James Billie doesn't like, he'll know no limits in stopping it...remember, this guy wrestles alligators!! No Joke! I don't recognize the other names on the list, but I think it's clear from both the beurocratic issues, as well as the specific tribal issues incident to each affected tribe, everything associated with the process should be executed with all of this in mind. I'd be happy to speak with the project's lawyer to discuss this further, if information beyond that set forth above would be helpful. Thanks! --Christie. ----- Forwarded by Christie Patrick/HOU/ECT on 03/15/2001 08:53 AM ----- roger_w_gunther@urscorp.com 03/15/2001 08:32 AM To: alice.weekley@enron.com cc: jordan.hunter@enron.com, stephen.veatch@enron.com, frank_s_wang@enron.com, christie.patrick@enron.com, ricardo_n_calvo@urscorp.com Subject: Calypso Tribal Letters Alice: Janus Research sent the enclosed letters to me; I understand that they have been modified per your discussions with them. Also enclosed is a summary document outlining recent revisions to Section 106 (National Historic Preservation Act). Regards, Roger (See attached file: Section 106 regs (1).doc)(See attached file: Fred McGhee 3-7-01.doc)(See attached file: James Billie 3-7-01.doc)(See attached file: Jerry Haney 3-7-01.doc)(See attached file: R. Perry Beaver 2-7-01.doc)(See attached file: Billie Cypress 3-7-01.doc) - Section 106 regs (1).doc - Fred McGhee 3-7-01.doc - James Billie 3-7-01.doc - Jerry Haney 3-7-01.doc - R. Perry Beaver 2-7-01.doc - Billie Cypress 3-7-01.doc ***********
## 854 State has only signed up 500 MW according to this article Sue Mara Enron Corp. Tel: (415) 782-7802 Fax:(415) 782-7854 ----- Forwarded by Susan J Mara/NA/Enron on 02/14/2001 09:57 AM ----- "Ronald Carroll" <rcarroll@bracepatt.com> 02/14/2001 08:11 AM To: <rreilley@coral-energy.com>, <acomnes@enron.com>, <jsteffe@enron.com>, <mary.hain@enron.com>, <smara@enron.com> cc: Subject: Fwd: California Governor Requests Additional $500 Million to Buy Energy ----- Message from "Tracey Bradley" <tbradley@bracepatt.com> on Wed, 14 Feb 2001 08:00:19 -0600 ----- To:\t"Paul Fox" <pfox@bracepatt.com> cc:\t"Andrea Settanni" <asettanni@bracepatt.com>, "Ronald Carroll" <rcarroll@bracepatt.com> Subject:\tCalifornia Governor Requests Additional $500 Million to Buy Energy California Governor Requests Additional $500 Million to Buy Energy Dion Nissenbaum , San Jose Mercury News, Calif. ( February 14, 2001 ) Feb. 14--SACRAMENTO, Calif.--As Gov. Gray Davis works to craft a bailout plan for California's two largest electric utilities, California's fledgling foray into the power business is coming at an ever more expensive price that is nearing $2 billion. For the second time in about a week, the Davis administration alerted lawmakers Tuesday that it needs another $500 million to buy energy on the costly short-term market while negotiators try to work out long-term contracts with power suppliers. That brings the two-month state price tag to about $2 billion, far exceeding early estimates that California would have to spend about $1 billion to launch its energy buying program. The news has left some lawmakers worried that the state is burning through money without making enough progress in resolving the underlying problems. With the bills piling up, Davis is looking to present Southern California Edison and Pacific Gas and Electric with a plan by week's end to rescue the two companies edging ever closer to bankruptcy. The state was forced to start buying power because the two utilities have exhausted their credit. For now, the governor's advisers appear to have decided on state ownership of the utility transmission lines as the foundation of any deal, according to sources involved in the negotiations. In a Tuesday press conference, Davis said he has not settled on transmission lines, but wants to make sure that the state gets something in return for rescuing the companies. "What I will propose, hopefully this Friday, will not be a bailout, it will be a buyout," Davis said. Buying the transmission lines has become the most-favored option in the Capitol in recent days as what one consumer activist called the "crown jewel" of any solution. A state Senate committee formally embraced the idea Tuesday by approving a bare-bones bill that encourages Davis to buy the transmission lines as part of any utility deal. Several issues must be settled before a transmission line deal is final. It still isn't clear what the lines are worth, or whether the utilities will have to pay capital gains taxes on the lines if they sell them to the state. And under the law, the federal government may have veto power over any transmission line deal. But negotiators expressed confidence they can resolve those issues. However, the state is still struggling to put together low-cost, long-term contracts with power companies that are essential to keeping electric bills close to current levels. When legislators gave Davis the power last month to sign the energy deals, they set aside $500 million to tide the state over until it could get the contracts in place. That came on top of another $1 billion from other funds that have been used to buy energy that now costs about $45 million a day. California won't be able to stop spending so much until it signs enough long-term contracts. But negotiations have been plodding along and so far the state has managed to come up with only about 500 megawatts -- enough to power a half million homes. "I am starting to get concerned about the pace at which they appear to be entering into contracts," said Assemblyman Fred Keeley, D-Santa Cruz. "It is slow and there don't appear to be many of them." Davis has released few details about the contracts, but has expressed optimism that his negotiators will be able to put together enough deals to solve the problems. "No one ever expected this to be a slam dunk because it's a tough negotiation," said Davis spokesman Steve Maviglio. "But we're making progress." Ultimately, the state is supposed to be reimbursed for its mounting expenditures in the spot market, under terms of a $10 billion power-buying bond bill approved by the legislature two weeks ago. The treasurer's office intends to sell the first of those bonds in May, and their proceeds will be transferred to the state general fund. Later bond issues will generate money to be used for the long-term contracts. Bond investors will be repaid by utility ratepayers, but it is not yet clear whether that will involve a rate increase. Staff writers Cheryl Devall, Mark Gladstone, Chris O'Brien and Hallye Jordan contributed to this report. ----- To see more of the San Jose Mercury News, or to subscribe to the newspaper, go to http://www.sjmercury.com (c) 2001, San Jose Mercury News, Calif. Distributed by Knight Ridder/Tribune Business News. ***********
## 855 ----- Forwarded by Ricardo Charvel/NA/Enron on 02/21/2001 04:50 PM ----- Ricardo Charvel 02/21/2001 04:47 PM To: Richard Shapiro/NA/Enron cc: Subject: Ken Lay in Cancun Rick, Ken Lay is going to be the Co-Chair of the Mexico Meeting of the World Economic Forum to be held in Cancun on Feb. 26 and 27. Rob Bradley asked me to prepare information and some talking points. I drafted a presentation which apparently Ken Lay liked when we met with him on Feb. 9th. Rob worked on making the wording better and came up with a final version of the talking points. Rob handed me the responsibility of assisting Ken Lay at Cancun and Rosalee suggested that I be there personally. I will be flying out on Sunday at noon and returning to Mexico on Tuesday late at night. I am attaching the agenda for the event, the power point presentation and the talking points in word. Talking points Slides Lay's Participation Agenda Rick, I could not thank you properly for your support since at the time of your call I was not alone. Kikumi and I are very grateful for your support and kindness. Thank you again, Ricardo ***********
## responsive
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## 'data.frame': 855 obs. of 2 variables:
## $ email : chr "North America's integrated electricity market requires cooperation on environmental policies Commission for Environmental Coope"| __truncated__ "FYI -----Original Message----- From: \t\"Ginny Feliciano\" <gfeliciano@earthlink.net>@ENRON [mailto:IMCEANOTES-+22Ginny+20Felic"| __truncated__ "14:13:53 Synchronizing Mailbox 'Kean, Steven J.' 14:13:53 Synchronizing Hierarchy 14:13:53 Synchronizing Favorites 14:13:53 Syn"| __truncated__ "^ ----- Forwarded by Steven J Kean/NA/Enron on 03/02/2001 12:27 PM ----- Suzanne_Nimocks@mckinsey.com Sent by: Carol_Benter@mck"| __truncated__ ...
## $ responsive: int 0 1 0 1 0 0 1 0 0 0 ...
## - attr(*, "comment")= chr "glb_entity_df"
## NULLif (!glb_is_separate_newent_dataset) {
glb_trnent_df <- glb_entity_df; comment(glb_trnent_df) <- "glb_trnent_df"
} # else glb_entity_df is maintained as is for chunk:inspectORexplore.data
if (glb_is_separate_newent_dataset) {
glb_newent_df <- myimport_data(
url=glb_newdt_url,
comment="glb_newent_df", force_header=TRUE)
# To make plots / stats / checks easier in chunk:inspectORexplore.data
glb_entity_df <- rbind(glb_trnent_df, glb_newent_df); comment(glb_entity_df) <- "glb_entity_df"
} else {
if (!glb_split_entity_newent_datasets) {
stop("Not implemented yet")
glb_newent_df <- glb_trnent_df[sample(1:nrow(glb_trnent_df),
max(2, nrow(glb_trnent_df) / 1000)),]
} else if (glb_split_newdata_method == "condition") {
glb_newent_df <- do.call("subset",
list(glb_trnent_df, parse(text=glb_split_newdata_condition)))
glb_trnent_df <- do.call("subset",
list(glb_trnent_df, parse(text=paste0("!(",
glb_split_newdata_condition,
")"))))
} else if (glb_split_newdata_method == "sample") {
require(caTools)
set.seed(glb_split_sample.seed)
split <- sample.split(glb_trnent_df[, glb_rsp_var_raw],
SplitRatio=(1-glb_split_newdata_size_ratio))
glb_newent_df <- glb_trnent_df[!split, ]
glb_trnent_df <- glb_trnent_df[split ,]
} else if (glb_split_newdata_method == "copy") {
glb_trnent_df <- glb_entity_df
comment(glb_trnent_df) <- "glb_trnent_df"
glb_newent_df <- glb_entity_df
comment(glb_newent_df) <- "glb_newent_df"
} else stop("glb_split_newdata_method should be %in% c('condition', 'sample', 'copy')")
comment(glb_newent_df) <- "glb_newent_df"
myprint_df(glb_newent_df)
str(glb_newent_df)
if (glb_split_entity_newent_datasets) {
myprint_df(glb_trnent_df)
str(glb_trnent_df)
}
} ## Loading required package: caTools## email
## 2 FYI -----Original Message----- From: \t"Ginny Feliciano" <gfeliciano@earthlink.net>@ENRON [mailto:IMCEANOTES-+22Ginny+20Feliciano+22+20+3Cgfeliciano+40earthlink+2Enet+3E+40ENRON@ENRON.com] Sent:\tThursday, June 28, 2001 3:40 PM To:\tSilvia Woodard; Paul Runci; Katrin Thomas; John A. Riggs; Kurt E. Yeager; Gregg Ward; Philip K. Verleger; Admiral Richard H. Truly; Susan Tomasky; Tsutomu Toichi; Susan F. Tierney; John A. Strom; Gerald M. Stokes; Kevin Stoffer; Edward M. Stern; Irwin M. Stelzer; Hoff Stauffer; Steven R. Spencer; Robert Smart; Bernie Schroeder; George A. Schreiber, Jr.; Robert N. Schock; James R. Schlesinger; Roger W. Sant; John W. Rowe; James E. Rogers; John F. Riordan; James Ragland; Frank J. Puzio; Tony Prophet; Robert Priddle; Michael Price; John B. Phillips; Robert Perciasepe; D. Louis Peoples; Robert Nordhaus; Walker Nolan; William A. Nitze; Kazutoshi Muramatsu; Ernest J. Moniz; Nancy C. Mohn; Callum McCarthy; Thomas R. Mason; Edward P. Martin; Jan W. Mares; James K. Malernee; S. David Freeman; Edwin Lupberger; Amory B. Lovins; Lynn LeMaster; Hoesung Lee; Lay, Kenneth; Lester Lave; Wilfrid L. Kohl; Soo Kyung Kim; Melanie Kenderdine; Paul L. Joskow; Ira H. Jolles; Frederick E. John; John Jimison; William W. Hogan; Robert A. Hefner, III; James K. Gray; Craig G. Goodman; Charles F. Goff, Jr.; Jerry D. Geist; Fritz Gautschi; Larry G. Garberding; Roger Gale; William Fulkerson; Stephen E. Frank; George Frampton; Juan Eibenschutz; Theodore R. Eck; Congressman John Dingell; Brian N. Dickie; William E. Dickenson; Etienne Deffarges; Wilfried Czernie; Loren C. Cox; Anne Cleary; Bernard H. Cherry; Red Cavaney; Ralph Cavanagh; Thomas R. Casten; Peter Bradford; Peter D. Blair; Ellen Berman; Roger A. Berliner; Michael L. Beatty; Vicky A. Bailey; Merribel S. Ayres; Catherine G. Abbott Subject:\tEnergy Deregulation - California State Auditor Report Attached is my report prepared on behalf of the California State Auditor. I look forward to seeing you at The Aspen Institute Energy Policy Forum. Charles J. Cicchetti Pacific Economics Group, LLC - ca report new.pdf ***********
## 5 ----- Forwarded by Steven J Kean/NA/Enron on 09/26/2000 04:20 PM ----- "Duncan Kincheloe" <dkincheloe@mpua.org> 09/26/2000 10:22 AM \t To: <skean@enron.com> cc: Subject: Fw: Tagging issue at NERC Security Subcommittee tomorrow Steve, I sent the below to you this morning at an apparently outdated email address. Your secretary was kind enough to update me so I'm forwarding it again. Duncan Kincheloe General Manager and CEO Missouri Public Utility Alliance 573-445-3279 Fax 573-445-0680 ----- Original Message ----- From: Duncan Kincheloe To: Steven Kean Sent: Tuesday, September 26, 2000 10:01 AM Subject: Tagging issue at NERC Security Subcommittee tomorrow Steve, I haven't been in touch since the DOE Reliability Task Force and hope this email address through Elizabeth is still the way to reach you. Incidentally, I was with Sue Tierney at a NARUC conference yesterday and we've started a new pool to wager when Congress will get this issue dealt with. Want in? Perhaps you remember that while we were serving on the Task Force I moved to EPRI to direct their government relations. Last year I moved back to Missouri to run the municipals' state joint action agency, which is why I'm writing. Part of our activity is operating a 19 city power pool (MoPEP) that has a three year supply contract of up to 40 mWh/hr with Enron. We've had some difficulties with delivery since a change in tagging was made in June. The issue and a proposed solution is explained in the attached three page "bullet statement" that we've just submitted to the new NERC Security Subcommittee that meets tomorrow. Based on his discussions with NERC staff, with a Mike Curry with Enron and other affected utilities, my chief operating officer for MoPEP is optimistic about prospects for this resolution. I would like to make sure of Enron's support. I'm told that Dick Ingersoll will be at the subcommittee meeting for Enron, and I know Dick from SPP meetings when I was on the Missouri Commission but don't have contact information on him now. I'd be very grateful if you could pass along a nod of benign interest in this. We'd greatly appreciate Enron's support for the proposed resolution of the tagging issue at the NERC Subcommittee tomorrow, mainly because it should avoid what might otherwise be a messy contract dispute, at FERC or elsewhere, between our organizations. Without getting into the details, my understanding of the issue under the energy purchase agreement has to do with whether the change in NERC tagging that was required on June 1 (the date when MoPEP started taking certain control area services from Western Resources) affects Enron's obligation under the agreement to deliver energy to the Associated Electric Cooperative transmission system. MoPEP's view is that Enron's obligations are unaffected, but some at Enron apparently have had a different view. Our belief is that the proposal that will be presented to the Security Subcommittee will resolve the tagging matter such that both Enron and MoPEP would regard any potential dispute on this issue as moot. Sorry for such short notice, but this resolution and taking it to the subcommittee came out of discussions in Atlanta Friday. Thanks for your consideration, Steve. Hope all is well with you. Duncan Kincheloe General Manager and CEO Missouri Public Utility Alliance 573-445-3279 Fax 573-445-0680 - MoPEP Tagging.doc ***********
## 11 Ditto Palmer's comments. Jeff Brown 09/27/99 03:20 PM To: Richard Shapiro/HOU/EES@EES, Steven J Kean/HOU/EES@EES, James D Steffes/HOU/EES@EES, Mark Palmer/Corp/Enron@Enron, Gary Foster/Corp/Enron@Enron cc: Subject: EEI - CUBR Press Release Attached is the draft press release prepared by EEI announcing the joint effort with our coalition (CUBR). I need your comments/questions/concerns by close of business on Sept. 29. Thanks, Jeff ***********
## 13 fyi ---------------------- Forwarded by Mark Palmer/Corp/Enron on 04/24/2000 09:01 AM --------------------------- From: Claudia Johnson@ENRON COMMUNICATIONS on 04/21/2000 02:39 PM PDT To: Joe Hirko/Enron Communications@Enron Communications, Kevin Hannon/Enron Communications@Enron Communications, Ken Rice/Enron Communications@Enron Communications, Kelly Kimberly/ENRON_DEVELOPMENT@ENRON_DEVELOPMENT, Mark Palmer/Corp/Enron@ENRON, Karen Denne/Corp/Enron@ENRON, Paula Rieker/Corp/Enron@ENRON cc: Subject: Enron is both old and new economy --- an article Enron is both Old and New Economy Saw opportunity that Canadian firms missed, Mathew Ingram says MATHEW INGRAM 04/18/2000 The Globe and Mail Calgary -- Old Economy, New Economy: In the past few weeks, one goes down while the other goes up, and then the next day they change places. For the nervous, there are a few companies that have a foot in both of these camps -- including one whose feet come with size large construction boots on them. That company is Houston-based Enron Corp., and it is everything that Canadian energy and pipeline companies could have been but aren't. Enron is one of the world's largest energy companies, with a market value of about $45-billion (U.S.) and interests in electric power generation, natural gas production, pipelines and related industries. But it is also a major player in a new kind of pipeline business -- the fibre-optic information pipeline business. Not only does it own a massive fibre network, but it is also a leader in the emerging market of bandwidth trading. Not that long ago, Enron was a sleepy old energy utility -- much like TransCanada PipeLines, for example. Then the company realized it could play a role in the market for new commodities such as fibre-optic bandwidth . Enron president Jeff Skilling said recently that in five or six years, he expects the bandwidth market "will be as large as the combined natural gas and electricity markets," or about $300-billion. It didn't take a genius to see that pipeline companies had an advantage when it came to building fibre networks: that is, ready-made routes. Railway and oil industry magnate Philip Anschutz was one of the early entrants in the market, having hit on the idea of laying fibre-optic cables along his railway rights-of-way -- an idea that became a $30-billion company called Qwest Communications (which is merging with US West). Williams Cos. had the same idea of laying fibre alongside its pipelines, and later sold that business to MCI WorldCom in 1995 for $2.5-billion. The company is now completing a new network through subsidiary Williams Communications. In Canada, Alberta-based construction company Ledcor started laying fibre as it was repairing tracks for CN Rail, and sold access to phone and Internet companies. That business became 360Networks, a separate unit that is expected to go public this week. So where were pipeline and energy companies like TransCanada and Nova and Enbridge (formerly IPL) and Westcoast while all this was going on? Well, Nova was busy trying to be a chemical and a pipeline company, and then it and TransCanada were busy fighting the Alliance pipeline project, and then they decided to merge, in one of the world's all-time worst merger deals. Now TransCanada has its hands full trying to replace the billions in market value it has managed to lose over the past year. Westcoast and Enbridge have focused on pipelines and related interests, and they have both done a good job of it -- but they remain tied to fundamentally low-margin businesses. Enron has swung for the fences, and that's why its stock has climbed from the $45 range last fall to about $65. Given its exposure to technology, Enron has suffered a little bit lately. Enron isn't the only one building the pipes for the information industry, mind you. Established companies such as Qwest and WorldCom own extensive networks, while newer entrants such as Global Crossing and 360Networks are building or buying them. Enron even has competition from energy firms such as BP Amoco and PG&E, who recently bought a stake in a company called Aerie Networks that is building a network using their pipeline rights-of-way. Six other U.S. energy companies have a similar joint venture. With all this building, some industry watchers have raised the spectre of a bandwidth glut, and it is conceivable there will be some kind of consolidation. But Enron is in a good position to be one of the consolidators -- and meanwhile, the company is taking advantage of the increase in demand for fibre capacity by setting up a bandwidth -trading market. This is another area where energy companies like Enron have an advantage. They are already accustomed to sophisticated trading and hedging of commodities such as pipeline capacity and natural gas. Enron , for example, is a leader in energy trading and risk management. What's so different about selling space on a fibre-optic pipeline? Not much. Can a single company explore, produce and ship natural gas, produce and sell electric power, and become a leader in Internet infrastructure all at the same time? That remains to be seen, but Enron appears determined to try -- and that's why brokerage firms such as Merrill Lynch say it has a chance to become "the General Electric of the New Economy." ***********
## 28 Melissa, As you know, as of Jan 01, 2000 the Wellhead Portfolio has been segregated from the rest of HPLC's Physical Portfolio. As a result of this segregation, it has become much easier to analyze and manipulate this data so that monthly "accrual value" can be determined. This analysis has resulted in the realization that the Wellhead Potfolio creates approximately $275,000 of accrual value per month. The main drivers of this accrual value are deals that have rolled into evergreen status, accrual term deals, actual volumes greater than booked, half cent bid to mid spread, incorrect markings, and value withheld from MTM because of accrual costs. Of the above, Greg Sharp will receive value associated with evergreen/accrual deals, actual volumes greater than or less than booked, and deals incorrectly marked. Tom will keep value associated with his half cent bid to mid and accrual value needed to pay accrual costs (such as gas quality issues). The breakout of this value has been on average 60% to Greg and 40% to Tom. The following is a breakdown of 1st quarter accrual value. January- Total Accrual Value = $266,504 Greg's Value Tom's Value Evergreen Value = $140,750 Bid to Mid = $105,000 Accrual Value = $53,000 Accrual Costs = $13,000 Actual Volumes>Booked = $60,000 $118,000 Incorrect Marking = ($105,000) Total Value = $148,750 February- Total Accrual Value = $289,305 Greg's Value Tom's Value Evergreen Value = $145,000 Bid to Mid = $100,000 Accrual Value = $62,000 Accrual Costs = $13,000 Actual Volumes>Booked = $65,000 $113,000 Incorrect Marking = ($95,000) Total Value = $177,000 March - Total Accrual Value = $292,145 Greg's Value Tom's Value Evergreen Value = $147,000 Bid to Mid = $106,000 Accrual Value = $59,000 Accrual Costs = $13,000 Actual Volumes>Booked = $66,000 $119,000 Incorrect Marking = ($100,000) Total Value = $172,000 Let me know if you have any questions. Eric Enron North America Corp. From: Eric Bass 04/07/2000 02:00 PM To: Melissa Graves/HOU/ECT@ECT cc: Subject: Re: Wellhead Accrual Income Melissa, Here are the accrual values for Q1. More details to follow. Jan - $266,504 Feb- $289,305 Mar - $292,145 Eric Enron North America Corp. From: Melissa Graves 04/07/2000 01:51 PM To: Eric Bass/HOU/ECT@ECT cc: George Weissman/HOU/ECT@ECT, Gregory L Sharp/HOU/ECT@ECT Subject: Wellhead Accrual Income Eric, Per Greg Sharp, he and Tom Martin discussed the Wellhead Accrual Income numbers today, and Effective Q1 2000, Greg will begin recognizing this accrual income in his financials. It is my understanding that the new Wellhead Portfolio in Sitara assists you in calculating this monthly number (which is estimated at $170,000 per month). Could you please provide the detail behind this calculation, or if this data is pulled directly from Sitara, George and I could meet with you to become familiar with this data extraction process. Thank you, Melissa X39173 ***********
## 37 Also, there was a confirm that came in Friday with Amerex for you Buying 1/day BOM (12/2-12/31) HeHub Gas Daily $7.00 Call Option at $0.26. The counterparty was Cinergy...Let me know if this is a good trade and we'll get it loaded into the system. Thanks, Susan ***********
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## 107 ---------------------- Forwarded by Jason Williams/HOU/ECT on 07/12/2000 03:53 PM --------------------------- j.slechta@pecorp.com on 07/12/2000 02:53:18 PM To:\tBarbara_G_Dillard@enron.com cc:\tjason.williams@enron.com, s.holler@pecorp.com, d.wear@pecorp.com Subject:\tFinal cuts for gas day of July 11, 2000 @ Will County for Peoples and North Shore and Manhatten for Peoples Barb, For the gas day of July 11, 2000, North Shore was cut 2,570 @ Will County. The original total purchase of 37,281 MMBtu/day (20,080 on Contract 102922 & 17,201 on Contract 101471) has been reduced to 34,711 MMBtu/day (20,080 on Contract 102922 & 14,631 on Contract 101471). The cut of 2,570 occurred on Contract 101471 (Enron upstream R0259F for 897 and R0255F for 1,673). North Shore will reduce its SIQ purchase from 10,000 to 7,430. Regarding Peoples Gas, the total amount purchased of 191,937 MMBtu/day (66,937 Baseload and 125,000 SIQ) has been cut @ Will County by 6,513. The Peoples Will County volume went from 37,340 to 30,827. Enron deliveries to Manhatten for Peoples went from 122,210 to 117,818. Enron contract R0259F was cut by 4,392. The cuts @ Will County and Manhatten total 10,905 MMBtu. Peoples will reduce its SIQ purchase of 125,000 to 114,095. There are minor cuts for July 12th. I will update everyone regarding the 12th once the gas day is closed. Jer ---------------------------------------------------------------- The information transmitted is intended only for the person or entity to which it is addressed and may contain confidential and/or privileged material. Any review, retransmission, dissemination or other use of, or taking of any action in reliance upon, this information by persons or entities other than the intended recipient is prohibited. If you received this in error, please contact the sender and delete the material from any computer. ***********
## 371 As a registered reader of PMA Online's Daily Power Report, we are happy to provide you with a free trial subscription to Restructuring Today until May 3. Please see yesterday's issue attached. As a paid subscriber you receive timely news the date of issue. Restructuring Today has distinctive industry news and analysis which PMA can't deliver from the wire services, and at only $487 for over 250 issues each year, we consider it a bargain. Act before May 9 to receive $50 off! Check it out, and if you'd like to subscribe, just give us your name, email and telephone number, hit "reply" and a subscription agent will contact you. You can also find a subscription order form on the back page of the newsletter. Requires Acrobat Reader 3.1 or higher, available free at: http://www.adobe.com/prodindex/acrobat/readstep.html#reader ( ) YES, I WANT TO SUBSCRIBE TO RESTRUCTURING TODAY IN PDF FORMAT FOR ONLY $437/year ($50 of the regular price for subscriptions orders placed before May 9). ( ) I AM INTERESTED IN A CORPORATE SUBSCRIPTION TO RESTRUCTURING TODAY (Provide telephone number) NAME: EMAIL*: TELEPHONE: ( ) PLEASE DO NOT SEND ME TRIAL SUBSCRIPTIONS IN THE FUTURE Thanks for reading! - rt010424pma.pdf ***********
## 491 ---------------------- Forwarded by Kayne Coulter/HOU/ECT on 03/12/2001 08:15 AM --------------------------- Lloyd Will 03/12/2001 07:23 AM To: Brent Hebert, Erik Serio/Corp/Enron@Enron, chris.lenartowicz@enron.com, Justin Laverell/Corp/Enron@ENRON, George Diaz/Corp/Enron@ENRON, Keith Comeaux/Corp/Enron@Enron, Smith L Day/HOU/ECT@ECT, Kayne Coulter/HOU/ECT@ECT, Corry Bentley/HOU/ECT@ECT cc: Subject: Help Desk Number FYI ---------------------- Forwarded by Lloyd Will/HOU/ECT on 03/12/2001 07:22 AM --------------------------- "Taylor, Chad" <CTaylor@ercot.com> on 03/09/2001 08:47:34 AM To: 1 Everyone <everyone@ercot.com> cc: Subject: Help Desk Number All, The Help Desk number has changed.˜ The new number is 3900.˜ Please discontinue use of the 3922 number.˜ If you are not in Taylor the number will be 512-248-3900. ˜ If you have any questions, please feel free to call. Chad Taylor ERCOT Help Desk Analyst Microsoft Certified Professional (512)248-3923 ctaylor@ercot.com ˜ ***********
## 545 FYI - Just received this from CSFB and am printing out a copy. I will take a look at it tomorrow. ---------------------- Forwarded by Stephanie Panus/NA/Enron on 02/27/2001 05:11 PM --------------------------- "Khosla, Anita" <anita.khosla@csfb.com> on 02/27/2001 05:02:17 PM To: "'stephanie.panus@enron.com'" <stephanie.panus@enron.com> cc: Subject: ISDA Master Agreement between Credit Suisse First Boston Internat ional and Enron Corp. Stephanie- Further to my recent email, please find attached a copy of the Enron Corp. Schedule "marked-up" with our comments which replica the ISDA CSFBi has recent executed with Enron Credit Limited. I will send you a copy of the executed ISDA Agreement once I have received Enron Credit Limited's approval. In the interim, please let me know if you have any comments on the Schedule. Kind regards Anita <<056ctr(csfb-ene).doc>> - 056ctr(csfb-ene).doc ***********
## 654 Dan, should we put more meat on this bone? Regards Delainey ---------------------- Forwarded by David W Delainey/HOU/ECT on 02/06/2001 12:17 PM --------------------------- Christopher F Calger 02/06/2001 11:26 AM To: David W Delainey/HOU/ECT@ECT cc: Subject: EES Voluntary Curtailment Proposal Dave, FYI. While the rest of the document is being pulled together I thought you would want to see the EES component. Chris ---------------------- Forwarded by Christopher F Calger/PDX/ECT on 02/06/2001 09:27 AM --------------------------- Scott Gahn@EES 02/06/2001 08:32 AM To: Christopher F Calger/PDX/ECT@ECT cc: Dave Roberts/HOU/EES@EES, Mary Sullivan/HOU/EES@EES, Mike D Smith/HOU/EES@EES, Mark Dobler/HOU/EES@EES, Gary Weiss/HOU/EES@EES, Dan Leff/HOU/EES@EES, Marty Sunde/HOU/EES@EES, Don Black/HOU/EES@EES Subject: EES Voluntary Curtailment Proposal Here is another version - minor changes based on comments. ***********
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## 840 I apologize - but I am resending this email to you. I had one small change to the second attachment. Please disregard the previous email from Steve Kean regarding Enron Statements. Thanks. ----- Forwarded by Maureen McVicker/NA/Enron on 02/05/2001 06:38 PM ----- Maureen McVicker 02/05/2001 06:25 PM To: Andrew S Fastow/Enron@EnronXGate, Ben Glisan/HOU/ECT@ECT, Cindy Olson/Corp/Enron@ENRON, Cliff Baxter/Enron@EnronXGate, Dan Leff/HOU/EES@EES, Danny McCarty/ET&S/Enron@Enron, David W Delainey/HOU/ECT@ECT, Greg Whalley/HOU/ECT@ECT, Harold G Buchanan/HOU/EES@EES, Mark Metts/NA/Enron@Enron, James A Hughes/ENRON_DEVELOPMENT@ENRON_DEVELOPMENT, James Derrick/Corp/Enron@ENRON, Jeff Skilling/Corp/Enron@ENRON, Jeffrey McMahon/Enron@EnronXGate, Jeffrey Sherrick/Corp/Enron@ENRON, Jeremy Blachman/HOU/EES@EES, Jim Fallon/Enron Communications@Enron Communications, John J Lavorato/Enron@EnronXGate, John Sherriff/LON/ECT@ECT, Ken Rice/Enron Communications@Enron Communications, Kenneth Lay/Corp/Enron@ENRON, Kevin Hannon/Enron Communications@Enron Communications, Karen S Owens@ees@EES, Louise Kitchen/HOU/ECT@ECT, Mark Frevert/NA/Enron@Enron, Mark Koenig/Corp/Enron@ENRON, Mark S Muller/HOU/EES@EES, Marty Sunde/HOU/EES@EES, Matthew Scrimshaw/LON/ECT@ECT, Michael R Brown/LON/ECT@ECT, Mike McConnell/HOU/ECT@ECT, Philippe A Bibi/HOU/ECT@ECT, Raymond Bowen/Enron@EnronXGate, Rebecca McDonald/ENRON_DEVELOPMENT@ENRON_DEVELOPMENT, Richard Causey/Corp/Enron@ENRON, Rick Buy/HOU/ECT@ECT, Rod Hayslett/FGT/Enron@ENRON, Stanley Horton/Corp/Enron@Enron, Steve Elliott/Enron Communications@Enron Communications, Steven J Kean/NA/Enron@Enron, Thomas E White/HOU/EES@EES, Wade Cline/ENRON_DEVELOPMENT@ENRON_DEVELOPMENT, gavyn.davies@gs.com, pghemawat@hbs.edu, ghamel@strategos.com, cwimmer@weeklystandard.com, llindsey@aei.org, portney@rff.org, stelzer@aol.com, rzoellick@gmfus.org cc: Subject: From Steve Kean - Enron Statements FROM STEVE KEAN: Attached are recent Enron statements regarding the situation in California. Solving the Power Crisis Enron's "re-sourcing" of its California Power customers Steve Kean's Senate Testimony ***********
## 842 fyi ----- Forwarded by Susan J Mara/NA/Enron on 11/08/2000 11:43 AM ----- Gary Ackerman <foothill@lmi.net> 11/06/2000 11:41 PM Please respond to foothill To: Bill Ross <billr@calpine.com>, Bob Anderson <Robert_Anderson@apses.com>, Carolyn Baker <cabaker@duke-energy.com>, CHARLES A MIESSNER <camiessn@newwestenergy.com>, Corby Gardin <jcgardin@newwestenergy.com>, curt hatton <curt.Hatton@gen.pge.com>, Curtis Kebler <Curtis_L_Kebler@reliantenergy.com>, Denice Cazalet <dcazalet@apx.com>, Greg Blue <gtbl@dynegy.com>, Jack Pigott <jackp@calpine.com>, Ken Czarnecki <Ken_J_Czarnecki@calpx.com>, Kent Wheatland <KEWH@dynegy.com>, "Klemstine, Barbara A(F56661)" <barbara_klemstine@apses.com>, Randy Hickok <rjhickok@duke-energy.com>, Rob Lamkin <rllamkin@seiworldwide.com>, Rob Nichol <rsnichol@newwestenergy.com>, robert berry <berry@apx.com>, Roger Pelote <roger.pelote@williams.com>, Sue Mara <smara@enron.com>, Jeff Crowe <jcrowe@apx.com>, Dan Douglass <douglass@arterhadden.com> cc: bcc: Subject: Draft Comments for Thursday Folks, Here is my first draft. My time trial shows it takes 6 min and 45 seconds to deliver. We need to cut. Dan, can you send me a front end to this that I will utilize to make copies? Send me the front end from last week's filing supporting EPSA's motion. I'll take comments up to 12 noon Wednesday. gba ============================= The Western Power Trading Forum (WPTF) is a California non-profit trade association of almost thirty members who sell, buy and exchange power throughout the Western U.S. We are pleased to provide comments on the following areas in response to your Order issued last week. 1. Price caps and their unintended consequences 2. Forward scheduling, penalties, mandatory must-buy, and 3. Independent Governing Boards. First, I would like to take this opportunity to express our overall gratitude to the Commission for issuing its Order. The members of WPTF have been bitterly frustrated by the events of the last summer, and equally upset by the actions of non-jurisdictional parties in their attempt to grab the steering wheel of restructuring, and drive it into the ground. As I listened to your comments last week, I took note of all the things you said, and the stated purpose of your actions. After listening to you, and reading the Order, I very much wanted to be here today, even if it only afforded me a brief 5-minutes of your time. I would have flown here from California even if you only allowed me one phrase to be uttered publicly. And that phrase would be "Thank you. Thank you for taking the bold step forward to revive California's wholesale power market. Thank you for having the courage to stand up to parochial interests which are entirely self serving, and absent of any national policy goals." This Commission took a stab at correcting California's problems utilizing several measures, one of which was a $150 price cap. Whereas we applaud the Commission's effort, we find that price caps send the wrong signal to both suppliers and load. We have found that price caps misallocate resources, and as your Staff report demonstrates, increases exports of power to other parts of the grid where no caps exists. We find that price caps discourage demand-side participation, and at the level proposed, will discourage much needed peaking generation resources. We also find that any price cap, but especially at the level you have proposed, will create unintended consequences. For example, when market bids exceed $150, sellers will avoid bidding into the ISO or the PX. Will the market solution be as simple as bidding instead into a competitive exchange to the PX, such as APX, or AlTrade? Will the liquidity of the bids suddenly flip from one market to another because of an arbitrary line below which the single price auction operates, and above which the bilateral market prevails? That is a huge hammer, a huge movement of tens of millions of dollars moving within the space of an hour from one trading vehicle to another. WPTF has been against price caps, and continues to believe that price caps do much damage, and do not protect consumers. The damages include prolonging high prices, reducing price discovery, and reducing the quantity of long-term offers that otherwise would afford consumers much-needed price protection. Average prices do not diminish with caps, in fact, as you know, August prices in California were much higher than last June, even though the buyer price cap imposed by the ISO Governing Board was $250 instead of $750. We recognize that the political reality is such that regulators don't like price spikes, and caps are a quick solution. If you feel that price caps must be imposed, then limit the damage. We believe that Commissioner Hebert's price cap proposal which starts at $250, and increments upward by specific amounts at specific times would be the least damaging. We also believe that a formulaic price cap that moves with respect to demand, or fuel price would be the most harmful. Market participants can not conduct business with so much uncertainty hanging in the balance. The Commission recognized on the matter of Governance that California needs regulatory stability, and the Commission is pursuing policy options to regain much needed stability. On the other hand, floating price caps work in the opposite direction. They reek havoc because they inject more uncertainty, and lessen stability. If price caps are at all necessary, stick to the least damaging approach. WPTF supports forward scheduling of demand, the imposition of real-time penalties, and we support elimination of the must-buy provision embedded in the California PUC Preferred Policy Decision. Underscheduling of load has been a practice that has lead to economic dislocation, and reduced grid reliability. Mandatory pre-scheduling will get the ISO back on track by prudently limiting the amount of load that can be exposed to real-time resolution. Also, by eliminating the must-buy provision, the UDCs can no longer complain about "supply not showing up". Their avenues for finding cost-effective supply will be unlimited. Finally, we support the replacement of the stakeholder boards with independent board members. Whereas we have been troubled by the antics of the ISO Governing Board, we must note that the PX Governing Board has not had the same type of problems. The PX Governing Board has done much better at finding solutions to thorny problems. But it is asking too much, we think, to spare one and eliminate the other. The actions of the ISO Governing Board have proven to be highly damaging to the credibility of electric restructuring. Other states eschew restructuring after observing the endless debates, the layers upon layers of State interference in the workings of the California ISO Governing Board, and the leverage exercised by voting blocks of Governors with interests totally removed from the well-being of the very institution on whose Board they serve. We are concerned that the rules you have laid out for replacing the stakeholder Governing Board may not be enough. There may be incredible pressure placed upon the existing Board members to select candidates that further the aims of those that claim to protect consumers, but in fact, are making a power grab. My organization promises the Commission that if we suspect, if we have evidence, if we detect, or otherwise observe actions by a few to undermine the independence of a new Board, I can assure you I will be back here raising my voice loudly in protest. The California market simply can not survive serving two masters. The jurisdictional tug of war must end quickly so that we all can move forward and pursue the goals and ambitions set out by this Commission in Orders 888, and 2000. Thank you. ***********
## 843 Dale McMaster called this afternoon to advise that the Power Pool Council agreed to delay any decision on the recently proposed rule changes (importers excluded from setting pool price and dispatch risk) for a period of two weeks. During this two week period Dale has been asked to obtain stakeholder input into ways to address concerns about whether the spot price in the Alberta Power Pool is a product of interaction among competitiors in a efficient market. In our discussions with the Power Pool and ADRD today and yesterday Enron expressed its willingness to work with the Power Pool to provide feedback on suggested rule changes but has also made it clear that we do not agree with the premise that there is a "problem" that requires "emergency" amendments to the Pool rules. Our position has been that Alberta is short supply, and significant changes to the rules and/or market design will only discourage new generation development. Dale McMaster had just left the Power Pool Council meeting when he called and had not yet put much thought into how to define the issue he has to address and what the process will be. We discussed the process and it appears he will likely receive comments from stakeholders at large and also assemble a small committee of 4-5 stakeholders (which would include Enron) to work with the Power Pool to consider possible changes to the Pool rules. Dale said he would try to pull together a "terms of reference" document that would define the mandate of the small committee by tomorrow. After my call with Dale McMaster, Peter Keohane and I spoke to Aleck Trawick and we have asked Blakes to assist us in three areas: 1) Once the terms of reference of this small committee are released (and assuming Enron is asked to participate on the committee) Blakes will assist us with preparing a letter that states that Enron's participation does not represent acknowledgement or agreement that the Power Pool Rules must be amended to address the concerns of some stakeholders about real time pricing in Alberta and that Enron's participation on the committee is on a without prejudice basis; 2) Enron needs to obtain the final memo from Frontier Economics in which Frontier provides its opinion on how the proposed rule changes: i) would introduce price discrimination into the Alberta electricity market, ii) would be contrary to the development of an efficient, fair and openly competitive market for electricity, iii) would compromise the independence of the Power Pool, and iv) would have the expected effect of artificially depressing the real time price of electricity in Alberta. Once Blakes receives a copy of the Frontier Economics memo, they will prepare a memo for Enron advising: i) whether the two week process of consultation by the Power Pool is in accordance with the standards required of the PPC as an administrative tribunal operating in Alberta; and ii) whether such rule changes offend the provisions of the EUA; 3) Blakes will advise Enron of the possible avenues of recourse (i.e. appeal to the AEUB, and/or legal action in the Court of Queen's Bench) in the event the proposed rule changes (or similar changes) are approved by the PPC at the end of this two week period. GPC and Eric Thode will also be consulted on this issue and obviously input from all those within Enron working on this project will be sought prior to any decision being made to pursue any possible legal remedies. Please advise if you have any comments. Regards, Rob ***********
## 845 Please see attached letter, same subject -- signed, hard copy to follow. <<SRP Change.doc>> - SRP Change In Scheduling Practices_.doc ***********
## 849 ** I inadvertently left this article out of the first distribution of articles. This story appeared on http://www.individual.com April 10, 2001 _________________________________________________________ [B] FULL/ Calpine confident PG&E will repay past-due power sales --Calpine has $267 mln accounts receivable with PG&E --Calpine has $68 mln note r <> Chicago, April 4 (BridgeNews) - Calpine Corp. said it is confident that Pacific Gas & Electric Co. will repay its past-due receivables for power sold to it by Calpine. Calpine said it has $267 million in accounts receivable with PG&E, as well as a $68 million receivable note that is not yet due. PG&E must cure its outstanding defaults and pay its outstanding balances in order to continue working with Calpine through its bankruptcy proceedings. --Blue Derkin, BridgeNews * * * The following is the text of today's announcement, with emphasis added by BridgeNews. BridgeStation users will find links to company data at the end: PG&E Bankruptcy Will Not Affect Calpine's Commitment to California Power Market /FROM PR NEWSWIRE SAN FRANCISCO 415-543-7800/ TO BUSINESS AND ENERGY EDITORS: PG&E Bankruptcy Will Not Affect Calpine's Commitment to California Power Market SAN JOSE, Calif., April 9 /PRNewswire/ -- In response to Pacific Gas and Electric Company's (PG&E) April 6th Chapter 11 filing, Calpine Corporation (NYSE: CPN), the San Jose, Calif.-based independent power company, stated it is confident that PG&E, through a successful reorganization, will be able to pay Calpine's Qualifying Facility (QF) subsidiaries for all past due power sales, in addition to electricity deliveries made on a going-forward basis. CALPINE'S QF SUBSIDIARIES SELL POWER TO PG&E UNDER THE TERMS OF LONG-TERM QF CONTRACTS AT ELEVEN FACILITIES, REPRESENTING NEARLY 600 MEGAWATTS OF ELECTRICITY FOR NORTHERN CALIFORNIA POWER CUSTOMERS. AS OF MARCH 31, 2001, CALPINE HAS RECORDED APPROXIMATELY $267 MILLION IN ACCOUNTS RECEIVABLE WITH PG&E, PLUS A $68 MILLION NOTE RECEIVABLE NOT YET DUE AND PAYABLE. THE COMPANY'S REMAINING CALIFORNIA OPERATIONS, TOTALING APPROXIMATELY 700 MEGAWATTS OF CAPACITY, PROVIDE ELECTRICITY TO MUNICIPALITIES AND OTHER CREDITWORTHY THIRD PARTIES. CALPINE'S QF FACILITIES ARE PART OF A 9,000-MEGAWATT QF SUPPLY THAT PROVIDE CALIFORNIA CUSTOMERS WITH A LONG-TERM SOURCE OF ELECTRICITY AT PRICES SIGNIFICANTLY BELOW CURRENT WHOLESALE PRICES. THIS CRITICAL POWER SUPPLY REPRESENTS APPROXIMATELY 33 PERCENT OF THE STATE'S POWER DEMAND. WITHOUT THESE CONTRACTS IN PLACE FOR THIS SUMMER, CALIFORNIA FACES THE PROSPECT OF MORE BLACKOUTS AND HUNDREDS OF MILLIONS OF DOLLARS IN INCREASED COSTS. FOR THESE QF CONTRACTS TO CONTINUE, PG&E MUST ASSUME THE CONTRACTS IN THE BANKRUPTCY PROCEEDINGS. IN ORDER TO ASSUME THESE CONTRACTS, PG&E WILL BE REQUIRED TO CURE ALL OUTSTANDING DEFAULTS, INCLUDING PAYING ALL PAST DUE AMOUNTS. IF PG&E FAILS TO ASSUME THE CONTRACTS, CALPINE'S QF SUBSIDIARIES WILL BE ABLE TO SELL POWER ON THE OPEN MARKET AND SEEK DAMAGES FROM PG&E FOR BREACH OF CONTRACT THROUGH THE BANKRUPTCY CLAIMS RESOLUTION PROCESS. "As the state's leading developer of new electric generating facilities, Calpine remains committed to providing innovative solutions for California's _________________________________________________________ Individual.com is the #1 provider of free, individualized news and information to business people over the Internet. Visit us at http://www.individual.com to browse the largest free collection of business, financial, industry, trade, and company-specific news and information on the web. This news story was sent by miyung buster through Individual.com. You will not receive email messages directly from Individual.com unless you register at http://www.individual.com. Get more headlines and stories like this delivered FREE to your desktop every business morning! Register at http://www.individual.com/welcome.shtml. Individual.com also brings you FREE news on your investments! Sign up at http://www.individual.com/welcome.shtml. ___________________________________________________________ Entire contents Copyright , 1999-2000, Individual.com\001v, Inc., 8 New England Executive Park, Burlington, MA, 01803, USA ***********
## 852 Congrats!! Ray Alvarez 03/22/2001 04:29 PM To: Richard Shapiro/NA/Enron@Enron cc: Subject: Supreme Decree Rick, I heard from my Bolivia regulatory team that the supreme decree is a reality and that it is very positive- including full recovery of the $100MM gas deferred account that we were hanging out on. I will provide more details as soon as I receive them. Ray ***********
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## 1 North America's integrated electricity market requires cooperation on environmental policies Commission for Environmental Cooperation releases working paper on North America's electricity market Montreal, 27 November 2001 -- The North American Commission for Environmental Cooperation (CEC) is releasing a working paper highlighting the trend towards increasing trade, competition and cross-border investment in electricity between Canada, Mexico and the United States. It is hoped that the working paper, Environmental Challenges and Opportunities in the Evolving North American Electricity Market, will stimulate public discussion around a CEC symposium of the same title about the need to coordinate environmental policies trinationally as a North America-wide electricity market develops. The CEC symposium will take place in San Diego on 29-30 November, and will bring together leading experts from industry, academia, NGOs and the governments of Canada, Mexico and the United States to consider the impact of the evolving continental electricity market on human health and the environment. "Our goal [with the working paper and the symposium] is to highlight key environmental issues that must be addressed as the electricity markets in North America become more and more integrated," said Janine Ferretti, executive director of the CEC. "We want to stimulate discussion around the important policy questions being raised so that countries can cooperate in their approach to energy and the environment." The CEC, an international organization created under an environmental side agreement to NAFTA known as the North American Agreement on Environmental Cooperation, was established to address regional environmental concerns, help prevent potential trade and environmental conflicts, and promote the effective enforcement of environmental law. The CEC Secretariat believes that greater North American cooperation on environmental policies regarding the continental electricity market is necessary to: * protect air quality and mitigate climate change, * minimize the possibility of environment-based trade disputes, * ensure a dependable supply of reasonably priced electricity across North America * avoid creation of pollution havens, and * ensure local and national environmental measures remain effective. The Changing Market The working paper profiles the rapid changing North American electricity market. For example, in 2001, the US is projected to export 13.1 thousand gigawatt-hours (GWh) of electricity to Canada and Mexico. By 2007, this number is projected to grow to 16.9 thousand GWh of electricity. "Over the past few decades, the North American electricity market has developed into a complex array of cross-border transactions and relationships," said Phil Sharp, former US congressman and chairman of the CEC's Electricity Advisory Board. "We need to achieve this new level of cooperation in our environmental approaches as well." The Environmental Profile of the Electricity Sector The electricity sector is the single largest source of nationally reported toxins in the United States and Canada and a large source in Mexico. In the US, the electricity sector emits approximately 25 percent of all NOx emissions, roughly 35 percent of all CO2 emissions, 25 percent of all mercury emissions and almost 70 percent of SO2 emissions. These emissions have a large impact on airsheds, watersheds and migratory species corridors that are often shared between the three North American countries. "We want to discuss the possible outcomes from greater efforts to coordinate federal, state or provincial environmental laws and policies that relate to the electricity sector," said Ferretti. "How can we develop more compatible environmental approaches to help make domestic environmental policies more effective?" The Effects of an Integrated Electricity Market One key issue raised in the paper is the effect of market integration on the competitiveness of particular fuels such as coal, natural gas or renewables. Fuel choice largely determines environmental impacts from a specific facility, along with pollution control technologies, performance standards and regulations. The paper highlights other impacts of a highly competitive market as well. For example, concerns about so called "pollution havens" arise when significant differences in environmental laws or enforcement practices induce power companies to locate their operations in jurisdictions with lower standards. "The CEC Secretariat is exploring what additional environmental policies will work in this restructured market and how these policies can be adapted to ensure that they enhance competitiveness and benefit the entire region," said Sharp. Because trade rules and policy measures directly influence the variables that drive a successfully integrated North American electricity market, the working paper also addresses fuel choice, technology, pollution control strategies and subsidies. The CEC will use the information gathered during the discussion period to develop a final report that will be submitted to the Council in early 2002. For more information or to view the live video webcast of the symposium, please go to: http://www.cec.org/electricity. You may download the working paper and other supporting documents from: http://www.cec.org/programs_projects/other_initiatives/electricity/docs.cfm?varlan=english. Commission for Environmental Cooperation 393, rue St-Jacques Ouest, Bureau 200 Montréal (Québec) Canada H2Y 1N9 Tel: (514) 350-4300; Fax: (514) 350-4314 E-mail: info@ccemtl.org ***********
## 3 14:13:53 Synchronizing Mailbox 'Kean, Steven J.' 14:13:53 Synchronizing Hierarchy 14:13:53 Synchronizing Favorites 14:13:53 Synchronizing Folder 'Inbox' 14:14:04 \t 23 item(s) added to offline folder 14:14:04 \t 1 item(s) changed read-state in offline folder 14:14:04 Synchronizing Folder 'Outbox' 14:14:04 Synchronizing Folder 'Sent Items' 14:14:05 \t 1 item(s) added to offline folder 14:14:05 Synchronizing Folder 'humor' 14:14:05 Synchronizing Folder 'Advertising' 14:14:05 Synchronizing Folder 'HR-data' 14:14:05 Synchronizing Folder 'performance management' 14:14:05 Synchronizing Folder 'federal legislation' 14:14:05 Synchronizing Folder 'Europe' 14:14:05 Synchronizing Folder 'Notes' 14:14:05 Synchronizing Folder 'India' 14:14:06 Synchronizing Folder 'Rice' 14:14:06 Synchronizing Folder 'Pipeline issues' 14:14:06 Synchronizing Folder 'EES' 14:14:06 Synchronizing Folder 'Enron Mentions' 14:14:06 Synchronizing Folder 'Active Markets List' 14:14:06 Synchronizing Folder 'Enrononline' 14:14:06 Synchronizing Folder 'HR stats' 14:14:06 Synchronizing Folder 'OPIC' 14:14:06 Synchronizing Folder 'market structure' 14:14:07 Synchronizing Folder 'aviation' 14:14:07 Synchronizing Folder 'Azurix' 14:14:07 Synchronizing Folder 'PRC' 14:14:07 Synchronizing Folder 'HR' 14:14:07 Synchronizing Folder 'security' 14:14:07 Synchronizing Folder 'internal communications' 14:14:07 Synchronizing Folder 'organizations and associations' 14:14:07 Synchronizing Folder 'press' 14:14:07 Synchronizing Folder 'Press Releases' 14:14:07 Synchronizing Folder 'Project California' 14:14:07 Synchronizing Folder 'Mexico' 14:14:08 Synchronizing Folder 'Brazil' 14:14:08 Synchronizing Folder 'Budget' 14:14:08 Synchronizing Folder 'internet sites' 14:14:08 Synchronizing Folder 'Project Summer' 14:14:08 Synchronizing Folder 'SEC' 14:14:08 Synchronizing Folder 'Environmental Issues' 14:14:08 Synchronizing Folder 'presentations' 14:14:08 Synchronizing Folder 'Calendar' 14:14:08 \t 1 item(s) updated in offline folder 14:14:08 Synchronizing Folder 'California' 14:14:08 Synchronizing Folder 'organizations and phone lists' 14:14:08 Synchronizing Folder 'california - demand buydown' 14:14:08 Synchronizing Folder 'california - emergency authority' 14:14:08 Synchronizing Folder 'California - excess profits tax' 14:14:09 Synchronizing Folder 'California - working group' 14:14:09 Synchronizing Folder 'California campaign' 14:14:09 Synchronizing Folder 'facilities services' 14:14:09 Synchronizing Folder 'California-PG&E bankruptcy' 14:14:09 Synchronizing Folder 'Caliornia - investigations' 14:14:09 Synchronizing Folder 'Enron Wind' 14:14:09 Synchronizing Folder 'campaign 2000' 14:14:09 Synchronizing Folder 'Canada' 14:14:13 Synchronizing Folder 'Iowa' 14:14:13 Synchronizing Folder 'Reg risk' 14:14:13 Synchronizing Folder 'Metals' 14:14:13 Synchronizing Folder 'enron credit' 14:14:13 Synchronizing Folder 'CFTC' 14:14:13 Synchronizing Folder 'PGE' 14:14:13 Synchronizing Folder 'Japan' 14:14:13 Synchronizing Folder 'Coal' 14:14:13 Synchronizing Folder 'florida' 14:14:13 Synchronizing Folder 'Journal' 14:14:14 Synchronizing Folder 'FERC' 14:14:14 Synchronizing Folder 'To Do' 14:14:14 Synchronizing Folder 'Contact list' 14:14:14 Synchronizing Folder 'Contacts' 14:14:14 Synchronizing Folder 'contributions' 14:14:14 Synchronizing Folder 'corporate communications' 14:14:14 Synchronizing Folder 'Corporate responsibility' 14:14:14 Synchronizing Folder 'organization' 14:14:14 Synchronizing Folder 'thank yous' 14:14:14 Synchronizing Folder 'PAC' 14:14:15 Synchronizing Folder 'telecom' 14:14:15 Synchronizing Folder 'OFCCP audit' 14:14:15 Synchronizing Folder 'Tax issues' 14:14:15 Synchronizing Folder 'Tasks' 14:14:15 Synchronizing Folder 'PR-Crisis Management' 14:14:15 Synchronizing Folder 'Strategy and objectives' 14:14:15 Synchronizing Folder 'RTO' 14:14:15 Synchronizing Folder 'Enron global markets' 14:14:15 Synchronizing Folder 'personnel' 14:14:15 Synchronizing Folder 'Enron Advisonry Council' 14:14:15 Synchronizing Folder 'staff meeting' 14:14:15 Synchronizing Folder 'Speeches' 14:14:15 Synchronizing Folder 'Gas' 14:14:15 Synchronizing Folder 'personal' 14:14:15 Synchronizing Folder 'Drafts' 14:14:16 Synchronizing Views 14:14:16 Synchronizing Forms 14:14:40 Done ***********
## 4 ^ ----- Forwarded by Steven J Kean/NA/Enron on 03/02/2001 12:27 PM ----- Suzanne_Nimocks@mckinsey.com Sent by: Carol_Benter@mckinsey.com 03/02/2001 12:04 PM \t To: skean@enron.com cc: Subject: California Power Markets Sorry that we haven't talked in some time. I thought that you would want to take a look at some analysis we have recently completed with regard to the California Power Crisis. You may find some of the analysis to be helpful. Let me know if you have any questions. (See attached file: 10209 zxd414.ppt) +-------------------------------------------------------------+ ***********
## 6 George "Herbert Hoover" Bush. "Robert Kean" <rkean@starband.net> on 07/07/2001 10:13:19 AM Please respond to <rkean@starband.net> To:\t"Alan Kean \\(E-mail\\)" <rex04@msn.com>, "'Doug & Karen Reiman \\(E-mail\\)'" <dkreiman@mcleodusa.net>, "Kathy Wedig \\(E-mail\\)" <kat.wedig@netzero.net>, "Melissa Kean \\(E-mail\\)" <kean@rice.edu>, "Phil Kean \\(E-mail\\)" <kean.philip@mcleodusa.net>, "'Steve & Melissa Kean \\(E-mail\\)'" <skean@enron.com> cc:\t"Diane Kean \\(E-mail 2\\)" <dkean@starband.net> Subject:\tFW: Dadisms Dadisms 1. Run for the roundhouse Nellie, he'll never corner you there. 2. Slick as catshit and twice as nasty. 3. Like shit through a goose. 4. Yeah, that's right, keep fiddlin' with that, you'll break it 5. Ho! Ho! she cried in excess wild and waved her wooden leg on high! 6. On accounta the war. 7. They'll have to shoot him on judgement day. 8. Then of course, there are the names: Katy, Devie, My Little Mochawk, Phil Jofus, and Bubba 9. Choke ass (referring to peanut butter) 10. If it tastes good there are no calories 11. Didn't know you were in town (as driver passes on the right, etc.) 12. Like a couple of cub bears (meaning Rob and Phil) 13. Washington D.C. District or Corruption 14. Have some onions for defense 15. He won't have the guts to do THAT again (when a bug hits the windshield) 16. Saps goin' up the tree 17. Big Al 18. Douglas Doorite 19. Must be a storm comin'; the kids are actin' up 20. Constipate (concentrate) 21. Like being in hell with your back broke 22. gourd cover (hat) 23. mitts, dogs, paws (hands or feet) 24. you're too young 25. Worthington (when we asked him where something was or where he was going) 26. overhose (blue jeans) 27. excruciating pain! (when we squeezed his hand) 28. That's a hell of a note 29. meat wagon (ambulance) 30. drappies (drapes) 31. grappies (grapes) 32. Whoa horsie! Just cuz you only got 3 legs. (when he was putting on the brakes in the car) 33. Jeana, Jeana corpastina, I'm in love with you 34. I love you anyway 35. Scuse my boardin' house reach. 36. Come on out and let me lay a wheel on ya! (in traffic) 37. Sit down and start passin' (at meal time) 38. That's a bit of all right. 39. Here, guard this with your life (when he handed something to us at a store) 40. SOL (shit out of luck) 41. I'll tell ya when your're 21. 42. fernilla, dawberry, shocoletta (vanilla, strawberry, chocolate) 43. straighten up, now. 44. Pardon me soldier. (when he burped) 45. I'm going to assume the position of a soldier. (when he was going to lay on the couch for a nap) 46. horses doovers 47. lemon meringoo pie 48. solft 49. go ahead, see if it bends (when someone pulled out in front of him) 50. Ioway State 51. Cedar Rapids, Cedar Rapids, come on down. Hotel Roosevelt center of town. 52. A hundred dogs going wild in the tobacco field. Does your cigarette taste difference? (a quote from the one Russian guy in Cascade) 53. Cut her loose and let her wander. 54. Get your dogs under the table. 55. "Shit!" cried the queen and a thousand loyal subjects strained their bowels. 56. Who's side are you on?! The power company? (when we left lights on) 57. That makes it soggy and hard to light 58. Full as a tick 59. A treat instead of a treatment 60. Intermittent Power ***********
## 7 See last story attached. Congratulations, it looks like you are shaking things up in typical Enron fashion. ---------------------- Forwarded by Steven J Kean/NA/Enron on 07/14/2001 03:41 PM --------------------------- From:\tAnn M Schmidt on 07/13/2001 05:13 PM To: cc:\t (bcc: Steven J Kean/NA/Enron) Subject:\tEnron Mentions Wisconsin Gas to Purchase Nine Mile Line From Northern Natural Gas PR Newswire, 07/13/01 Commodities Review: LME Copper Hit By Another Stock Rise Dow Jones Commodities Service, 07/13/01 LME Base Metals: Copper Pressured Down By Stock Build Dow Jones Commodities Service, 07/13/01 UK: Telecoms, drugs drive FTSE 100 rally, Vodafone leads. Reuters English News Service, 07/13/01 Futures Contract Covering Coal Debuts in New York (Correct) Bloomberg, 07/13/01 Enron: Panacea or Pariah? Modern Metals, July 2001 Wisconsin Gas to Purchase Nine Mile Line From Northern Natural Gas 07/13/2001 PR Newswire (Copyright (c) 2001, PR Newswire) Line Will Enhance Competition in Southeast Wisconsin and Use Existing Facilities MILWAUKEE, July 13 /PRNewswire/ -- Wisconsin Gas, Northern Natural Gas and Guardian Pipeline announced today that Wisconsin Gas has signed an agreement with Northern Natural Gas (NNG) to purchase a nine mile, high pressure natural gas pipeline in Walworth and Waukesha counties. The purchase price is $5 million. The proposed transaction will require approval from federal and state regulators. The pipe, known as the Eagle line, is located southeast of the Kettle Moraine Forest near Eagle, Wis. The existing line has been part of the NNG interstate transmission system. Under the new ownership, the line will become part of the Wisconsin Gas distribution system and will interconnect with Guardian Pipeline as well as the NNG system. "Northern Natural Gas is pleased to be a part of a creative, cost-effective solution to provide additional gas service to southeastern Wisconsin," said Dave Neubauer, vice president of business development and marketing, Northern Natural Gas. "This transaction enables Wisconsin Gas and its customers to obtain natural gas supplies from Guardian or NNG. Providing reliable, economical choices for consumers will benefit all of the parties involved." "We saw this as an opportunity to minimize environmental impact and still realize the benefits of increased competition in the natural gas market," said James Schott, senior vice president, Wisconsin Gas. "The purchase of the Eagle line will enable Wisconsin Gas to further our goal of providing cost-efficient natural gas service to our customers." As a result of the purchase Guardian Pipeline, the 141-mile interstate pipeline from Joliet, Ill. to Ixonia, Wis., will no longer build an 8.5-mile lateral to Eagle. The lateral was part of the originally certified Guardian Pipeline. This portion of the line would have generally run parallel to the current Northern Natural pipeline. "This sale is a win-win situation for Guardian and consumers," said George Hass, project manager of Guardian Pipeline. "The Eagle line purchase by Wisconsin Gas saves Guardian time and resources by taking away the need to build the 8.5-mile lateral and allows all three companies to create a better environment for competition in the natural gas marketplace." The existing gate stations at LaGrange and Eagle will remain in-service for regulation purposes. Odorization and measurement will take place at a new gate station that will be constructed at the interconnect with Guardian. This new gate station will be called the Bluff Creek station and will be located near Whitewater. The Bluff Creek gate station will be constructed during the construction of Guardian Pipeline. Northern Natural Gas, a subsidiary of Enron Corp., with a market area capacity of about 4.3 Bcf/d, provides natural gas transportation services to utility customers in the upper Midwestern United States through its approximately 16,000 miles of pipeline. Guardian Pipeline is a partnership of three Midwestern energy companies: CMS Energy, based in Dearborn, Mich.; Wisconsin Energy, headquartered in Milwaukee, Wis.; and Viking Gas Transmission Co., a wholly owned subsidiary of Xcel Energy Inc., and located in St. Paul, Minn. Wisconsin Electric-Wisconsin Gas, the principal utility subsidiary of Wisconsin Energy Corp. (NYSE: WEC), serves more than one million electric customers and more than 960,000 natural gas customers throughout Wisconsin and Michigan's Upper Peninsula. Visit our company's Web site at www.WE-WG.com. Learn about Wisconsin Energy Corp. by visiting www.WisconsinEnergy.com MAKE YOUR OPINION COUNT - Click Here http://tbutton.prnewswire.com/prn/11690X86200271 /CONTACT: Media only: Kelly Farr of CMS Energy, 313-436-9253; or Megan McCarthy of Wisconsin Energy, 414-221-4444; or Gina Taylor of Northern Natural Gas, 713-853-7681/ 17:01 EDT Copyright © 2000 Dow Jones & Company, Inc. All Rights Reserved. Commodities Review: LME Copper Hit By Another Stock Rise By Mark Long and Steve McGrath Of DOW JONES NEWSWIRES 07/13/2001 Dow Jones Commodities Service (Copyright (c) 2001, Dow Jones & Company, Inc.) LONDON -(Dow Jones)- Copper futures traded in London fell Friday, as a further influx of stocks into official warehouses put the copper market under pressure and dragged down the rest of the base metal complex. Three-month copper traded on the London Metal Exchange ended $6 down from Thursday's late kerb close at $1,554.50 a metric ton. An increase of 7,475 tons of copper into LME warehouses Friday brought total stocks up to a 12-month high of 550,300 tons. This pressured prices in early trade down to test $1,550/ton support, although prices recovered slightly in later trade. Unconfirmed market talk said Enron is behind much of the week's stock rise, in an effort to ease nearby supply tightness and to alleviate the large short positions it is thought to have built up on the July-for-a-week spread, which is currently showing a $6-$8/ton backwardation. The July futures contract will switch over to cash Monday. Enron declined to comment on the market talk Friday. A backwardation is a pricing structure in which deliveries to be made in the near future are more expensive then those set for a more distant delivery. The opposite situation is called contango. "The spreads are still tight. The major longs are still reluctant to lend, with no signs of that (tightness) dissipated," said Kevin Norrish, an analyst with Barclays Capital in London. Dealers and analysts said they expect further stock increases next week. The entire base metals complex ignored Thursday's 237-point rally in the Dow Jones Industrial Average, dealers said. "Copper has been sensitive to moves in the stock market, but not this time," Norrish said. In fact, now it appears that base metals markets ignore equities markets when they rally, but follow stocks down when they slump, he added. European Grain Futures See Profit-Taking, But More Gains Expected European grain and oilseed futures were hit by a bout of profit-taking and pre-weekend position-squaring Friday, after rising to long-time highs, brokers said. However, world and domestic crop concerns, the strength of the dollar and general bullish sentiment will carry the markets to fresh highs short term, they said. On the London International Financial Futures and Options Exchange, the benchmark November feed wheat contract ended just 25 pence up at GBP82/ton, after hitting a new two-year high of GBP83/ton in early trade. Meanwhile, Matif November rapeseed futures fell EUR2.75 on the day to EUR265/ton, after hitting a three-year high of EUR271/ton in early trade. The U.K. wheat crop and the French rapeseed crop are forecast to be well down on last year, and wheat and canola crops in some other major producing countries are also expected to fall. Meanwhile, the strength of the dollar is allowing European grain and oilseed traders to up prices and still remain competitive against U.S. imports and dollar-denominated grains on the world market. -By Mark Long and Steve McGrath, Dow Jones Newswires; 44-20-7842-9358; mark.long@dowjones.com Copyright © 2000 Dow Jones & Company, Inc. All Rights Reserved. LME Base Metals: Copper Pressured Down By Stock Build 07/13/2001 Dow Jones Commodities Service (Copyright (c) 2001, Dow Jones & Company, Inc.) LONDON -(Dow Jones)- London Metal Exchange three-month copper ended Friday's late kerb lower compared with Thursday, pressured by another stock build, dealers said. (LME three-month metals prices in dollars a metric ton at 1600 GMT, with the previous late kerb close in parentheses. Comex copper at 1641 GMT in cents a pound, with the previous close in parentheses.) Copper 1,554.50 (1,560.50) Tin 4,452.50 (4,502.50) Aluminum 1,452.25 (1,458.50) Zinc 871.50 (877.50) Nickel 5,925.00 (6,012.50) Lead 462.50 (465.00) Comex Sep Copper 70.75 (70.80) An increase of 7,475 tons of copper Friday brought total stocks up to a 12-month high of 550,300 tons. This pressured prices in early trade down to test $1,550/ton support before rising slightly to close at $1,554.50/ton, down from Thursday's close of $1,560.50/ton. Unconfirmed market talk said Enron is behind much of the week's stock rise, in an effort to ease nearby supply tightness and to alleviate the large short positions it is thought to have built up on the July-for-a-week spread, which is currently showing a $6-$8/ton backwardation. The July futures contract will switch over to cash Monday. Enron declined to comment on the market talk. A backwardation is a pricing structure in which deliveries to be made in the near future are more expensive then those set for a more distant delivery. The opposite situation is called contango. "The spreads are still tight. The major longs are still reluctant to lend, with no signs of that (tightness) dissipated," said Kevin Norrish, an analyst with Barclays Capital in London. Dealers and analysts said they expect further stock increases next week. The entire base metals complex ignored Thursday's 237-point rally in the Dow Jones Industrial Average, dealers said. "Copper has been sensitive to moves in the stock market, but not this time," Norrish said. In fact, now it appears that base metals markets ignore equities markets when they rally, but follow stocks down when they slump, he added. Aluminum also ended the late kerb lower Friday. News of the restart of some smelters has dampened sentiment, despite a stock drawdown of around 8,000 tons since the end of last week, dealers said. "The stock drawdowns are fairly small percentage-wise," said Standard Bank analyst Robin Bhar, adding that they would need to be much larger to significantly improve market sentiment. "A test of $1,435/ton cannot be ruled out, though we would expect forward buying to prevent a break below this level for the time being," Barclays said in a market report. Zinc continued its slump, falling to a seven-and-a-half-year low, mainly on technicals. Technicals also hurt nickel, which rose slightly after breaking $5,900/ton support but still closed at its lowest level since April. Nickel's fall was "just on some speculative liquidation and short selling, mostly chart-based," Bhar said. -By Mark Long, Dow Jones Newswires; +44-20-7842-9356; mark.long@dowjones.com Copyright © 2000 Dow Jones & Company, Inc. All Rights Reserved. UK: Telecoms, drugs drive FTSE 100 rally, Vodafone leads. By Camila Reed 07/13/2001 Reuters English News Service (C) Reuters Limited 2001. LONDON, July 13 (Reuters) - Britain's largest companies raced higher at the close on Friday fired by telecoms and drug stocks as they bounced back from Wednesday's 16-week low. Index heavyweights Vodafone and GlaxoSmithKline provided 27 of the day's 55-point gain, while technology firms wobbled. However, the telecoms giant surged 4.1 percent to 157-1/4p. The FTSE 100 finished 55.4 points or one percent firmer at 5,537.0, having jumped 1.66 percent on Thursday after seven consecutive losing sessions, with gainers outnumbering losers by three to one. Stocks steamed higher and refused to be derailed by sluggish U.S. retail sales figures for June, which showed only a meagre rise, and a weaker Wall Street, but volumes were thin at 1.4 billion shares. By London's 1530 GMT finish the blue chip Dow Jones industrial average was down five points, while the tech-laden Nasdaq Composite Index was flat, having streaked up in the previous session. Mike Lenhoff and Simon Rubinsohn at money managers Gerrard said that the UK equity market was deeply oversold and long-term this presented a buying opportunity for UK equities. DRUGS SEE-SAW Drugs reversed an earlier decline with Nycomed Amersham among the day's top performers, up nearly four percent at 536-1/2p and AstraZeneca rising over one percent. Mining giant Anglo American moved into the FTSE 100 top ten gainers with a three percent rise, as investors sought refuge in mining stocks as a cyclical play. Another major gainer was British business services firm Hays Plc up 3.4 percent. The shares leapt on market talk that Germany's Deutsche Post was looking to make a bid for it, dealers said. Both Deutsche Post and Hays declined to comment. Firmer crude prices gave support to the oil sector. BP rose 0.6 percent to 572 pence and Shell also added 0.6 percent to 581-1/2p lifting the index by four points. FTSE MIXED PICTURE But dealers and analysts said it would be a mixed picture going forward with the market scrutinising data for signs of a sustained economic recovery or a lapse in consumer confidence. "This week we had a really bloody day on Wednesday and then the opposite on Thursday, so we're going to be on a rollercoaster still. It's very fragile but it does show the potential of the market," said Foreign & Colonial director of UK equities David Manning. "It's not only the holiday period, it's also that the market can make you look an idiot one day to the next. In that sort of circumstance the temptation is to do very little," he said. ABN Amro strategist Gareth Williams said the market would make modest progress over the next month or so, with moves downward quite likely although the trend would be upwards over the course of the year. TECHS DIP Thursday's 5.3-percent rally in the Nasdaq failed to bring any cheer to UK technology shares. Logica and Colt Telecom slid four percent, while microchip designer ARM fell 3.9 percent to 223 pence trimming Thursday's 13 percent jump. The techMARK index of technology shares rose 0.98 percent to 1,602.87. Elsewhere on the downside, UK holiday operator Airtours lost nearly 10 percent of its value after Swiss peer Kuoni issued a profit warning. Among second liners, Baltimore Technologies closed up 3.3 percent. It initially rallied 22 percent after the Irish Internet security firm said it received an approach from an unlisted British-based company for an all-paper deal to combine the companies. The FTSE 250 Mid-cap index finished 5.1 points stronger at 6,163.8. Among small-caps, Paladin Resources' shares jumped 10 percent after the UK oil explorer said that major shareholder U.S. oil giant Enron had sold the whole of its 40-million-share stake via HSBC bank for 44 pence a share. Britain's Litho Supplies Plc slumped 42.5 percent after the company, which supplies printing and graphic arts products, said that sales in the first six months of 2001 were on target, but profits would likely be below expectations due to tough market conditions and pressure on margins. Copyright © 2000 Dow Jones & Company, Inc. All Rights Reserved. Futures Contract Covering Coal Debuts in New York (Correct) 2001-07-13 08:48 (New York) Futures Contract Covering Coal Debuts in New York (Correct) (Corrects location of FirstEnergy Corp. in 10th paragraph of story that moved yesterday.) New York, July 12 (Bloomberg) -- The New York Mercantile Exchange today began trading a futures contract for low-sulfur coal, a fuel that President George W. Bush said will play a major role in meeting electricity demand over the next two decades. The contract's debut came after prices for coal, considered the dirtiest of power-plant fuels, doubled in the past year, as demand from power plants outstripped mine production. Coal for September delivery opened at $42 a ton and settled at $40.75, as 98 contracts were traded, exchange officials said. ``We had a big run-up in coal prices this past winter,'' said Andy Ozley, who buys coal for Atlanta-based Mirant Corp.'s six coal plants in New York, Illinois, Maryland and Virginia. ``This contract affords us a way to mitigate our exposure'' to large price swings. The Bush administration said in an energy policy report this spring that coal would be the dominant fuel for electricity generation through 2020. Bush gave an added boost to the fuel when he retreated from a campaign pledge to restrict carbon dioxide emissions, implicated in global warming. Coal plants are responsible for a third of the nation's carbon dioxide emissions. They also produce sulfur dioxide, which causes acid rain, and nitrogen oxides, which cause smog. The Nymex started the futures contract to give mining companies and utilities a way to hedge their risks in the $33 billion coal market. Producers such as Peabody Energy Corp. and Arch Coal Inc., whose shares have soared in the past year, have said they would trade the futures. New Plants Last year's quadrupling of prices for natural gas -- a cleaner-burning power-plant fuel -- prompted many generators to ramp up output from their coal plants. Utility coal stockpiles dwindled, and prices have risen to their highest levels since at least 1989. So far this year, more than a dozen companies have announced plans to build coal-fired plants, including Kansas City, Missouri- based Kansas City Power & Light Co. and Portland, Oregon-based PacifiCorp. Although coal is used to produce 52 percent of the nation's electricity, few plants were built during the 1990s because of the high costs of building them and the difficulty of complying with federal and local environmental regulations. Demand for coal has eased in recent weeks as below-normal temperatures in the U.S. Midwest and Northeast reduced demand for air conditioning, said Jim Parks, who buys coal for six power plants in Ohio and Pennsylvania as director of fuels at Akron, Ohio-based FirstEnergy Corp. ``The summer's been pretty mild so far -- you haven't seen any big heat waves'' in regions with the most coal generation, he said. Big Sandy The Nymex futures contract calls for the delivery of 1,550 tons of low-sulfur coal to the mouth of the Big Sandy River near Huntington, West Virginia. From there, the fuel would be loaded onto Ohio River barges and shipped to power plants in the U.S. and possibly Europe. The new contract is tailored to producers in Appalachian states that account for about 40 percent of U.S. production, including West Virginia, Kentucky and Pennsylvania, the No. 2, 3 and 4 coal producers. The industry lacks a futures contract for coal from Western states that account for 47 percent of the nation's production, including the No. 1 producer, Wyoming. Avoiding the Past The exchange, which earns commissions on trades, hopes coal futures will avoid the fate of other contracts it has started over the past few years. Its six electricity futures contracts have fizzled, as has its Middle East sour crude oil contract, which debuted in May 2000. ``I said yesterday, if you traded 100 to 200 contracts, that would be a really a good day,'' said Chris Casale, vice president of energy trading at Dynegy Inc. in Houston, which trades coal and buys the fuel for its six power plants in New York and Illinois. ``It came out sort of on target.'' Most of today's trading was originated by large energy marketing companies, he said, although he declined to say whether Dynegy traded any contracts. Enron Corp., the largest energy trader, and Duke Energy Corp., the largest U.S.-based utility holding company, have said they would trade coal futures. Exchange officials have said the coal market has the potential to reach 5,000 trades a day after a year of trading. The Nymex's 17-year-old crude oil futures contract averages about 66,700 trades a day, and its 11-year-old natural gas contract averages about 33,500. Wary of Coal ``The coal market is still lacking liquidity,'' said Anthony McAuley, a floor broker at ABN Amro Inc., who said he bought five coal contracts for a client at $42 a ton -- the market's first transaction. ``You probably need to see a little more participation'' from industry coal buyers and sellers for the market to take off. Independent floor traders, known as locals, are wary of trading coal futures without assurances that the industry will be active as well. ``Floor traders lost millions'' in electricity futures when some were caught with high-priced contracts and nobody in the industry to sell them to, he said. ``Because of that, people are a little bit tentative about coal,'' said Tom Schiff, an independent crude oil trader. ``We're here to help make liquidity but we can't tackle all the risk.'' Modern Metals, July 2001 Giant energy concern now turns its attention to steel. The company's aggressive posture has begged controversy. Industry reactions vary from hand-wringing to open arms. Still others characterize Enron as a "non-event." By Michelle Martinez Arjona, Editor-in-Chief, and Paul Hohl, Contributing Editor These days, saying the word "Enron" in a room full of metals executives is like screaming "fire" in a crowded theater. For most in the steel industry, the idea of a futures market is enough to raise a few eyebrows. But when a major market maker with annual revenues of 10 times the market cap of the U.S. steelmaking industry stakes a claim (for 2000 alone, Enron logged revenues of $101 billion--that's with a "b"), a lot more than eyebrows are likely to be raised. After a bold presentation from Jeff McMahon, president and CEO of Enron Industrial Markets, at the SSCI annual convention in May (in which he accused steel mills of treating their service center customers as "annoyances"), industry leaders rushed to understand this latest market contender. Friend or foe? Competitor or partner? These are just a couple of the questions surrounding Enron's aggressive push into the metals industry. Judging from the service center and mill executives Modern Metals queried, there are as many answers as there are opinions in the industry. Reactions ranged from fear to acceptance to downright resentment. At least one mill was reticent to discuss Enron at all--still others seemed to meet the issue with unaffected shrugs. If the saying is true that all publicity is good publicity, the buzz that Enron has generated would make even a Hollywood spin doctor envious. What's the hubbub? Although products such as natural gas and energy--Enron's major business platform--have been traded as commodities for years, until Enron's appearance a formal forward market for steel has never existed. According to McMahon, the absence of a liquid and transparent market has resulted in "uninformed investment decisions" that have exacerbated the current oversupply situation, and left companies exposed to the hazards of price volatility. Enron's answer is to offer a variety of financial hedging products along with forward contracts in hot-rolled, cold-rolled and galvanized (plate and long products are soon to follow), locking in prices for as many as five years out. Enron contends that it can act as a "risk intermediary" in steel transactions, ensuring healthier profit margins and lowering the cost of capital. Certain grades of steel are commodities, McMahon insists, and as such should be bought and sold on a commodity market basis, instead of the strategic relationships that now exist between the mills and consumers. Enron has been offering steel online, and via phone and fax, since November of last year. "Before you add capacity, before you make an investment decision, you'll be able to see a three, five, maybe even a 10-year forward price of steel," McMahon stated, "and if you want to hedge that investment you can do that." The company plans to buy steel at a floating price and then sell it to steel consumers at a fixed rate, making money on the spread. In February, Greg Hermans, VP of steel trading at Enron, reported to AMM that it was offering U.S.-made 10-gauge, 48-in. wide hot-rolled for April delivery in Chicago for $225 a ton. Hermans said Enron was buying the same product for April delivery at $215 a ton. Eventually, Enron sees the development of four different marketplaces: the Northeast, the Midwest (or Chicago area), the Gulf Coast (or Houston area), and the West Coast. Enron will hedge against spot price spikes through equity and physical holdings in domestic mills. The company closed on Huntco's Arkansas-based cold-rolling mill in June. "It's a big portfolio," McMahon said. "We wanted to put together a portfolio of supply and demand. In some cases, we'll own assets--hopefully we'll own some term contracts with domestic mills. To the extent that imports are a part of the equation, we'll be a part of that market. We may purchase capacity from somebody, not necessarily the assets, but toll slab through a hot strip mill. All of those combined is how we do our business." Steel mills can benefit, McMahon said, by entering into a multi-year "physical off take" with Enron, thus giving steel mills a degree of certain volume and on product specification. Enron would guarantee purchase of a specified amount of tonnage, and use a floating price or a floor price to guard against market shocks. Whether mills would be willing to divert capacity to the commodity grades being bought and sold by Enron remains uncertain. At least one mill executive privately said no . . . at least for now. Nucor president, CEO and vice chairman, Dan DiMicco also sees little value in Enron's proposal. "What we need to have is stable pricing at levels where the most efficient mills can make a good return on their capital and further invest in new technologies and equipment," he stated. "I define true value as helping the industry to become stronger. It doesn't do any good to maintain a lack of price volatility at $200 a ton." Keith Busse, CEO at Steel Dynamics, echoed that sentiment in a recent New Steel article. "If you're trying to get volatility out at $320 [per ton], great," he said. "But at $230, you want to stick your fingers down your throat." For the service centers Service centers, McMahon suggested, could benefit immediately. "The only way [distributors] are able to hedge themselves is to go out and buy inventory," he explained. "That takes capital, and cash, and space. We can offer a financial product that gives the exact same protection, but doesn't require any of the above. "We can hold those inventories and price the steel at the time of delivery to the service centers," he continued, "so their 4- or 5- percent net margins are somewhat certain." "I do think that Enron is providing a service for the industry . . . " commented Dave Lerman, CEO of Steel Warehouse, South Bend, Indiana. But he pointed out that "how [it] moves forward will be interesting. If they just move forward with hedge instruments . . . that's independent of whether you run your business well by having a good supplier that matches up with the demands of your customers. I think [Enron] will be a good thing for some people and a non-event for others." Beyond hedging, McMahon pointed out, firm and enforceable contracts could become a key benefit to service centers. As one Midwestern distributor source commented, "One of the terrible things that happens in our business is that end users demand long-term pricing, beyond what anybody can foresee. When they anticipate the market is going up, or that it has hit a low point, they push harder for it--as any one would. But historically, they don't honor the deals." In a down market, he explained, customers demand lower prices. Noncompliance can result in orders, but no releases, and a "don't call us, we'll call you" attitude from customers. The consequence for service centers, he said, are price reductions on the way down and on the way up. "If Enron brings integrity to both sides of this market," he concluded, "that would be a very big plus for the steel industry." But integrity, another Midwest service center exec countered, has got nothing to do with it. In his estimation, Enron is like a profiteering carpetbagger, "trying to make a buck in between the producer and the user. That's all we need right now," he declared, "somebody trying to squeeze out a few more dollars." But, he recognized, "we're all going to have to deal with it." Don McNeeley, president and CEO of Chicago Tube & Iron, indicated that some good could come of a heavy-hitter like Enron introducing new ideas into the industry, but acknowledged that people might be offended by somebody picking the so-called "low-hanging fruit" of the steel market in the current economy. "What do you think the mills are going to do when you come to them for just the peripheral items?" he queried. "My concern would be that the price would go up on [those items]." McNeeley also questioned the wisdom of distributors relinquishing control of their inventories. "In distribution, 60 percent of our net worth is inventory, 30 percent is accounts receivable, and 10 percent is plant property and equipment," he related. "Our single largest asset is our inventory. If a distribution company surrenders control of its inventory to a third party, does not that distributor, in effect, surrender its own sovereignty?" Only just begun Questions abound regarding Enron's role as a physical supplier of steel as well. As one Minnesota-based service center exec put it, "are they ready to deal with problems?" Although Enron has expressed interest in being a physical supplier, Dave Lerman explained, "I think that's going to be a lot more difficult to implement." Enron would have to consider specific qualities, coil sizes, nuances of chemistry, surface quality, and formability. "Most customers require better than standard tolerances," he pointed out. "These are all issues that might complicate commodity sales." Delivery is another issue. As a physical supplier, Enron has virtually guaranteed just-in-time delivery, but as one service center source said, "the guarantee is good when the material shows up at my door. What happens if it doesn't show up? I disappoint a customer. What good does it do to sue [Enron] if I've lost a customer?" Mill executives have questioned whether Enron is a potential competitor. "They see Enron as someone likely to buy foreign steel in order to deliver it to a U.S. customer," said Chuck Bradford, principal of Bradford Research and a long-time industry observer. He cautioned against seeing Enron as too much of a physical supplier, speculating that the future of Enron, similar to the LME, will be in hedging. "You may see some [physical transactions] to start with," Bradford predicted, "just to get the ball rolling. But I don't see that as the way the market will develop." McMahon has been consistent in saying that Enron has little interest in becoming a steel manufacturer. The real value, he said, is in a transparent, liquid market. "We know the products work, we know the market wants them. Can any one particular mill or mills prevent that? We don't think so," he said. "It's not an 'if', it's a 'when'." The jury is still out on just how soon that "when" will come, and what those effects will be for the steel industry. And for all of the debate surrounding the issue, it seems a bit soon to be hitting the panic button. As Don McNeeley recounted, "I can recall similar controversy over a speech a guy gave about 15-17 years ago. That guy's name was Ken Iverson, and he had this concept called a mini-mill. Look at it now." ***********
## 8 We plan to send this out Friday. Cynthia will provide copies to the Hill as she sees appropriate. Any comments? ***********
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## 212 I'm told that the four states conducting the inquiry into UtiliCorp's gas purchases and capacity release methodologies have a conference call with UtiliCorp on Friday. UCU has admitted an "accounting error" to Michigan and has offered a $100,000 refund. The PSC is looking for something more in the area of $20 million. You remember that Nick Schwartz was in Lansing the same day we were last month giving a deposition. Although it was supposed to be on another subject it quickly turned to this issue. A copy of the deposition is attached below. Mike McGowan 11/15/2000 11:01 AM To: Lon Stanton/ET&S/Enron@ENRON cc: Subject: Re: States Investigation of UCU Gas Purchasing Lon - I too was surprised and wondered if that was why Nick was there. Anyway you can DISCREETLY find out what's happening (from Joel?? or Vince???). ***********
## 335 <<Julie Simon Document.DOC>> ============================================================================Th is e-mail message and any attached files are confidential and are intended solely for the use of the addressee(s) named above. This communication may contain material protected by attorney-client, work product, or other privileges. If you are not the intended recipient or person responsible for delivering this confidential communication to the intended recipient, you have received this communication in error, and any review, use, dissemination, forwarding, printing, copying, or other distribution of this e-mail message and any attached files is strictly prohibited. If you have received this confidential communication in error, please notify the sender immediately by reply e-mail message and permanently delete the original message. To reply to our email administrator directly, send an email to postmaster@dsmo.com Dickstein Shapiro Morin & Oshinsky LLP http://www.legalinnovators.com - Julie Simon Document.DOC ***********
## 398 Harry has asked that Robert Nuestaedter and myself represent our area in developing EGA regulatory hedge strategies where EES has taken a significant retail position. I have met with the Utility Risk Management folks to let them know we will help develop and execute strategies. Some of the goals the URM desk has set for themselves includes regulatory participation. We may be able to draw from them resources if needed. I know all of you have been part of ongoing strategy development/execution so I just wanted to let you know what we are doing on this end. Government Affairs Support (that would be Harry et. al.) has put together a business plan for EES support which includes some recurring reporting and utility tariff (rate schedule) analysis. Harry will be presenting some of this info next week. Just a heads-up. ***********
## 439 Please send comments to Elizabeth ASAP. ----- Forwarded by Steven J Kean/NA/Enron on 01/21/2001 03:50 PM ----- Elizabeth Ivers 01/21/2001 03:23 PM To: Mark Palmer/Corp/Enron@ENRON, Steven J Kean/NA/Enron@Enron cc: Mark Koenig/Corp/Enron@ENRON Subject: California A starting point for the California section of the Conference Call script. Please comment. Thanks. ***********
## 584 PennFuture's E-cubed is a commentary biweekly email publication concerning the current themes and trends in the energy market. PennFuture would like to wish all of our readers Season's Greetings. And what could be better this season than the gift of a clean, prosperous and healthy future for Pennsylvania, its environment and its citizens? Give the gift of a PennFuture membership today! Visit our website at www.pennfuture.org - vol3no24_122001.doc ***********
## 708 Attached is a draft of a legal risk memo prepared in connection with the proposed sale of another block of COPEL shares. As you can imagine, it looks much like the one prepared in January. Lance and I have discussed this latest memo, but it has not yet been distributed to anyone else. Please give me any comments you may have and let me know if there are others you recommend be added to the distribution list. ***********
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## 848 Executive Summary: £ Legislators in both parties are scared about a fiscal meltdown and looking for a realistic Plan B £ The Keeley plan is the most likely vehicle and would include the 30% generator haircuts and revenue bonds backed by a dedicated rate component £ Despite uncertainty over dealing with transmission lines and continued oppostion from Burton and his consumer group allies, the Keeley plan will probably pass the legislature £ The generators will not support the plan until they get clearer guarantees from the state that investigations and lawsuits will end £ PG&E could get the same offer as SoCal through the bankruptcy court. 1. Budget Binge Could Scare Legislators into a Deal According to sources, the Assembly is "terrified" of the current holes in the budget due to buying power. The impact of the energy crisis is reportedly causing panic among some legislators. This panic appears to be moving a SoCal bailout plan forward. Sources report that a modified Plan B deal is "not that far off"; it will likely take one to two weeks (more likely closer to two) to move a deal through the Assembly and the Senate and to the governor's desk once it is introduced. 2. What Would the New Plan B Look Like? - This modified Plan B deal, which will originate in the Assembly, will almost certainly include a dedicated rate component to pay SoCal's undercollect. Revenue bonds will be issued securitized by this dedicated rate component. It is also very likely that the generators would be offered 70 cents on the dollar for what they are owed immediately, with the possibility of more if they win litigation. (Sources report that the state is "fairly confident" that at least most generators would not be successful in winning this additional money.) Assembly Speaker Keeley reportedly remains the driving force behind this plan, and it is being drawn up from within his office. 3. Transmission Lines - It remains unclear at this time whether a transmission line purchase or option to purchase will be put into the deal, since this is a major point of contention between the Democrats and Republicans. According to sources, the Assembly is "working on this." 4. Plan Looks Good to Pass Both Assembly and Senate - According to sources, the vote count for passing a deal containing the above components (except the transmission line purchase, which is not yet settled) "looks very good." The vote in the Senate would be "very close." Senator Burton still opposes a bail-out deal, but he reportedly might not stand in the way of others from voting for the plan, making its passage more likely. Even opponents of the plan (including Burton and the consumer advocates) say that the odds of a modified Plan B passing the Senate are better than 50-50. These opponents are reportedly "pessimistic" about their chances of stopping a Plan B from passing at this point. 5. Caveat: Generators Are Waiting For Guarantees That the Witch Hunt Will End - As reported previously, sources continue to believe that generators would be reluctant to take a 70 cent-on-the-dollar haircut without the state offering them relief from further prosecution. (Without this relief, the state could give them 70 cents, then try to take away even more.) Sources report that even opponents of the plan accept that the generators should be offered this relief from prosecution. Thus, it appears likely at this time that most generators would accept a 30 percent haircut if they were paid immediately. Also, sources believe that the securitization on the dedicated rate component offered in the modified Plan B would be sufficient to pay back those generators who successfully litigated to win more than 70 cents on the dollar. - Sources believe that SoCal Edison's creditors are likely to forebear from filing an involuntary bankruptcy long enough for this plan to go through, since it would pay back most of what they are owed. 6. PG & E Could Also Be Dealt In - If this plan succeeds, it will almost certainly be taken to the bankruptcy court and offered to PG&E as part of a reorganization plan. This plan can be offered at any time within PG&E's "period of exclusivity." This is a 90-day period (though it is often extended) following the filing of a bankruptcy petition. - However, sources report that the state is concerned about how to approach the bankruptcy court. Based on Supreme Court precedent (which legal sources within the state have been reviewing), the state would likely lose its sovereign immunity if it entered as a party into the bankrutpcy court. This is a problem because the stakes in this case are so high. 7. Bailout Bill Would Require Follow Legislation For A New Rate Structure - Even if the state passes and signs a SoCal bailout, it would still need to pass a rate structure that would allow SoCal and PG&E to flow through cost increases to prevent them from once again facing bankrtupcy. Therefore, this plan does not make the crisis go away. ***********
## 850 I am resending an email that I sent earlier but was returned. ----- Forwarded by Mark Crowther/AP/Enron on 03/27/2001 05:25 PM ----- Mark Crowther 03/27/2001 05:07 PM To: Steven J Kean/HOU/EES@EES, James D Steffes/NA/Enron@Enron, Steve Walton/HOU/ECT@ECT, Stephen D Burns/Corp/Enron@ENRON, Tom Briggs/NA/Enron@Enron, Richard Shapiro/NA/Enron@Enron, Mark Schroeder/LON/ECT@ECT, Paul Dawson/Govt. Affairs/LON/ECT@ECT, Peter Styles/LON/ECT@ECT cc: Nicholas O'Day/AP/Enron@Enron Subject: Brattle Group report on Japanese Electricity Market Gentlemen, In relation to the planned visit to London, Washington DC and Houston by Nick O'Day and myself over the next week to discuss regulatory issues in Japan and our plan for a competitive Japanese market, please find attached the latest draft of the report produced by the Brattle Group. We are very interested in hearing your comments on the ideas in this paper and how they relate to Enron's experience and policy in general. Best regards Mark Crowther Public Affairs Enron Japan ***********
## 851 Steve, The Secretary of Energy Ernesto Martens used to be the CEO of VITRO, our client in for the Monterrey Project. He was a board member until the day of his appointment. He knows Enron well. He used to be the President and CEO of CINTRA the holding company for Mexicana and Aeromexico airlines. When I worked at McKinsey I was engaged in projects with CINTRA for two years. Martens has a reputation of being a good and tough manager. He delivers. On the other hand he also has a reputation of being stubborn and not a good negotiator. We still have to convince him of the proposed electric sector reorganization. The good news is that Fox used many of the phrases that we wrote in the documents for the transition team. Apparently he is following the script. When he talks about no privatization he limits it by saying: there will be no sell out of CFE's current assets and by ensuring that the sector will be open to competition. Dionisio P,rez- Jacome was appointed President of the FERC. He is a classmate of Steffes from the Kennedy School and a good friend of mine. All of the team supporting him is Luis Tellez's team. The undersecretaries, the president of the CRE (FERC) the directors of CFE and LFC. The only new face is the Director of PEMEX who used to be the President and CEO of DuPont in Mexico. Raœl Mu\017oz was very helpful in promoting electric deregulation. About a year ago he organizad a trip to Altamira in the state of Tamaulipas in which he invited members of Congress to visit the DuPont and the BASF plants there so they could see the size of the investments and what was at risk if deregulation did not take place. Jaime Alatorre and I were the only non Congress, non-DuPont or BASF people invited. He and Alatorre are very very good friends. I spent some time with him yesterday at a conference in which I presented a document on energy and economic development and he invited me to his office to discuss competition in natural gas. CFE and LFC have the same directors which may make change more difficult btu it is inevitable. Best, Ricardo Steven J Kean 12/04/2000 04:11 PM To: Ricardo Charvel/NA/Enron@Enron cc: Subject: Energy Analysis How are the appointments turning out from your perspective? ----- Forwarded by Steven J Kean/NA/Enron on 12/04/2000 04:11 PM ----- "Nike Papadopoulos" <nikep@edelmanmexico.com.mx> 12/04/2000 11:22 AM Please respond to nikep To: <max.yzaguirre@enron.com>, <skean@enron.com>, <rshapiro@enron.com>, <eric.thode@enron.com>, <jaime.alatorre@enron.com>, <Ricardo.Charvel@enron.com>, <jaime.williams@enron.com>, <sirvin@enron.com>, <msabine@enron.com>, <agustin.perez@enron.com> cc: <aventura@edelmanmexico.com.mx> Subject: Energy Analysis Attached you will find last week's energy analysis which includes information about Vicente Fox's Energy team. Thank you. - 24Nov-3Dec.DOC ***********
## 853 ----- Forwarded by Steven J Kean/NA/Enron on 03/19/2001 07:50 AM ----- Christie Patrick@ECT 03/15/2001 10:02 AM To: Alice Weekley/ENRON_DEVELOPMENT@ENRON_DEVELOPMENt, Jordan Hunter/FGT/Enron@ENRON, Stephen Veatch/Enron@EnronXGate, Frank S Wang/Corp/Enron@Enron, ricardo_n_calvo@urscorp, roger_w_gunther@ursc cc: Steven J Kean/NA/Enron@Enron, Danny McCarty/ET&S/Enron@Enron, Mike McConnell/HOU/ECT@ECT, Kelly Kimberly/Enron Communications@Enron Communications, Lauren Iannarone/NY/ECT@ECT, Mark Palmer/Corp/Enron@ENRON Subject: Calypso Tribal Letters Friends, The statute should be reviewed by whichever lawyer is supporting the project. As I mentioned to Alice Weekley, I am unfamiliar with the recent amendments referenced requiring the notice for projects that do not actually cross reservation property. The amendment does not surprise me though. In the past, for example, during the Transwestern expansion in '91, we developed a comprehensive NAGPRA plan with tribes whose reservations we crossed, yet neighboring tribes were included--the thinking by the various State Historic Preservation officers was that unearthed remains (bones, funerial objects, pottery pieces, holy objects, etc) may have belonged to tribes other than those from whose reservation they were ultimately unearthed. It made for a VERY VERY long and complicated plan and consultation process....not only when objects were unearthed, but simply putting the required anticipatory plan and procedures in place PRIOR to construction. (It gives me a chuckle: my late husband, Leonard Hilton, who was in charge of the TW project, is likely rolling in his own grave at the mere thought of Enron having to put another of these plans together..Haha!!) As I said, I've not read the amendment , but hopefully it only legally requires 'notice'---yet, in my nearly 20 years of practicing law, I've rarely seen a 'notice' that didn't crack the door or sound the alarm of further buerocratic opportunity! For your further information, I have met Billie Ray Cypress personally and he's a decent and seemingly reasonable guy with a huge casino on the rez ; yet these personal attributes frequently go by the wayside when it comes to 'perceived economic opportunity'. Chief James Billie,[chief of the Seminoles, 2nd largest/2nd wealthiest tribe in North America] on the other hand, is shrewd in business, dictitorial in management, and personally , has an ego bigger than Texas, behavior to match, and is proud of it!! --which makes him a ton of fun to be with if he likes you--he wrestles alligators, writes rock and roll and "plays"in whatever stage opportunity is presented him--as long as he can be the center of attention. The Seminoles have not only a tribal web site, James Billie has his own!! Check it out--it's a hoot!! He has been chief for about 25 years (and I'm guessing he's maybe in his early 50's). The Seminoles have recognized significant economic development under James, but he is definitely a "My way or the highway" kind of guy--he loves you, hates you, or ignores you--and he has a tribal council that is respectfully terrified of him. If any activity smacks of anything James Billie doesn't like, he'll know no limits in stopping it...remember, this guy wrestles alligators!! No Joke! I don't recognize the other names on the list, but I think it's clear from both the beurocratic issues, as well as the specific tribal issues incident to each affected tribe, everything associated with the process should be executed with all of this in mind. I'd be happy to speak with the project's lawyer to discuss this further, if information beyond that set forth above would be helpful. Thanks! --Christie. ----- Forwarded by Christie Patrick/HOU/ECT on 03/15/2001 08:53 AM ----- roger_w_gunther@urscorp.com 03/15/2001 08:32 AM To: alice.weekley@enron.com cc: jordan.hunter@enron.com, stephen.veatch@enron.com, frank_s_wang@enron.com, christie.patrick@enron.com, ricardo_n_calvo@urscorp.com Subject: Calypso Tribal Letters Alice: Janus Research sent the enclosed letters to me; I understand that they have been modified per your discussions with them. Also enclosed is a summary document outlining recent revisions to Section 106 (National Historic Preservation Act). Regards, Roger (See attached file: Section 106 regs (1).doc)(See attached file: Fred McGhee 3-7-01.doc)(See attached file: James Billie 3-7-01.doc)(See attached file: Jerry Haney 3-7-01.doc)(See attached file: R. Perry Beaver 2-7-01.doc)(See attached file: Billie Cypress 3-7-01.doc) - Section 106 regs (1).doc - Fred McGhee 3-7-01.doc - James Billie 3-7-01.doc - Jerry Haney 3-7-01.doc - R. Perry Beaver 2-7-01.doc - Billie Cypress 3-7-01.doc ***********
## 854 State has only signed up 500 MW according to this article Sue Mara Enron Corp. Tel: (415) 782-7802 Fax:(415) 782-7854 ----- Forwarded by Susan J Mara/NA/Enron on 02/14/2001 09:57 AM ----- "Ronald Carroll" <rcarroll@bracepatt.com> 02/14/2001 08:11 AM To: <rreilley@coral-energy.com>, <acomnes@enron.com>, <jsteffe@enron.com>, <mary.hain@enron.com>, <smara@enron.com> cc: Subject: Fwd: California Governor Requests Additional $500 Million to Buy Energy ----- Message from "Tracey Bradley" <tbradley@bracepatt.com> on Wed, 14 Feb 2001 08:00:19 -0600 ----- To:\t"Paul Fox" <pfox@bracepatt.com> cc:\t"Andrea Settanni" <asettanni@bracepatt.com>, "Ronald Carroll" <rcarroll@bracepatt.com> Subject:\tCalifornia Governor Requests Additional $500 Million to Buy Energy California Governor Requests Additional $500 Million to Buy Energy Dion Nissenbaum , San Jose Mercury News, Calif. ( February 14, 2001 ) Feb. 14--SACRAMENTO, Calif.--As Gov. Gray Davis works to craft a bailout plan for California's two largest electric utilities, California's fledgling foray into the power business is coming at an ever more expensive price that is nearing $2 billion. For the second time in about a week, the Davis administration alerted lawmakers Tuesday that it needs another $500 million to buy energy on the costly short-term market while negotiators try to work out long-term contracts with power suppliers. That brings the two-month state price tag to about $2 billion, far exceeding early estimates that California would have to spend about $1 billion to launch its energy buying program. The news has left some lawmakers worried that the state is burning through money without making enough progress in resolving the underlying problems. With the bills piling up, Davis is looking to present Southern California Edison and Pacific Gas and Electric with a plan by week's end to rescue the two companies edging ever closer to bankruptcy. The state was forced to start buying power because the two utilities have exhausted their credit. For now, the governor's advisers appear to have decided on state ownership of the utility transmission lines as the foundation of any deal, according to sources involved in the negotiations. In a Tuesday press conference, Davis said he has not settled on transmission lines, but wants to make sure that the state gets something in return for rescuing the companies. "What I will propose, hopefully this Friday, will not be a bailout, it will be a buyout," Davis said. Buying the transmission lines has become the most-favored option in the Capitol in recent days as what one consumer activist called the "crown jewel" of any solution. A state Senate committee formally embraced the idea Tuesday by approving a bare-bones bill that encourages Davis to buy the transmission lines as part of any utility deal. Several issues must be settled before a transmission line deal is final. It still isn't clear what the lines are worth, or whether the utilities will have to pay capital gains taxes on the lines if they sell them to the state. And under the law, the federal government may have veto power over any transmission line deal. But negotiators expressed confidence they can resolve those issues. However, the state is still struggling to put together low-cost, long-term contracts with power companies that are essential to keeping electric bills close to current levels. When legislators gave Davis the power last month to sign the energy deals, they set aside $500 million to tide the state over until it could get the contracts in place. That came on top of another $1 billion from other funds that have been used to buy energy that now costs about $45 million a day. California won't be able to stop spending so much until it signs enough long-term contracts. But negotiations have been plodding along and so far the state has managed to come up with only about 500 megawatts -- enough to power a half million homes. "I am starting to get concerned about the pace at which they appear to be entering into contracts," said Assemblyman Fred Keeley, D-Santa Cruz. "It is slow and there don't appear to be many of them." Davis has released few details about the contracts, but has expressed optimism that his negotiators will be able to put together enough deals to solve the problems. "No one ever expected this to be a slam dunk because it's a tough negotiation," said Davis spokesman Steve Maviglio. "But we're making progress." Ultimately, the state is supposed to be reimbursed for its mounting expenditures in the spot market, under terms of a $10 billion power-buying bond bill approved by the legislature two weeks ago. The treasurer's office intends to sell the first of those bonds in May, and their proceeds will be transferred to the state general fund. Later bond issues will generate money to be used for the long-term contracts. Bond investors will be repaid by utility ratepayers, but it is not yet clear whether that will involve a rate increase. Staff writers Cheryl Devall, Mark Gladstone, Chris O'Brien and Hallye Jordan contributed to this report. ----- To see more of the San Jose Mercury News, or to subscribe to the newspaper, go to http://www.sjmercury.com (c) 2001, San Jose Mercury News, Calif. Distributed by Knight Ridder/Tribune Business News. ***********
## 855 ----- Forwarded by Ricardo Charvel/NA/Enron on 02/21/2001 04:50 PM ----- Ricardo Charvel 02/21/2001 04:47 PM To: Richard Shapiro/NA/Enron cc: Subject: Ken Lay in Cancun Rick, Ken Lay is going to be the Co-Chair of the Mexico Meeting of the World Economic Forum to be held in Cancun on Feb. 26 and 27. Rob Bradley asked me to prepare information and some talking points. I drafted a presentation which apparently Ken Lay liked when we met with him on Feb. 9th. Rob worked on making the wording better and came up with a final version of the talking points. Rob handed me the responsibility of assisting Ken Lay at Cancun and Rosalee suggested that I be there personally. I will be flying out on Sunday at noon and returning to Mexico on Tuesday late at night. I am attaching the agenda for the event, the power point presentation and the talking points in word. Talking points Slides Lay's Participation Agenda Rick, I could not thank you properly for your support since at the time of your call I was not alone. Kikumi and I are very grateful for your support and kindness. Thank you again, Ricardo ***********
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## $ email : chr "North America's integrated electricity market requires cooperation on environmental policies Commission for Environmental Coope"| __truncated__ "14:13:53 Synchronizing Mailbox 'Kean, Steven J.' 14:13:53 Synchronizing Hierarchy 14:13:53 Synchronizing Favorites 14:13:53 Syn"| __truncated__ "^ ----- Forwarded by Steven J Kean/NA/Enron on 03/02/2001 12:27 PM ----- Suzanne_Nimocks@mckinsey.com Sent by: Carol_Benter@mck"| __truncated__ "George \"Herbert Hoover\" Bush. \"Robert Kean\" <rkean@starband.net> on 07/07/2001 10:13:19 AM Please respond to <rkean@starban"| __truncated__ ...
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sample.split(org_entity_df[, glb_rsp_var_raw], SplitRatio=glb_max_obs), ]
org_entity_df <- NULL
}
# if (nrow(glb_newent_df) > glb_max_obs) {
# warning("glb_newent_df restricted to glb_max_obs: ", format(glb_max_obs, big.mark=","))
# org_newent_df <- glb_newent_df
# glb_newent_df <- org_newent_df[split <-
# sample.split(org_newent_df[, glb_rsp_var_raw], SplitRatio=glb_max_obs), ]
# org_newent_df <- NULL
# }
}
if (nrow(glb_trnent_df) == nrow(glb_entity_df))
warning("glb_trnent_df same as glb_entity_df")
if (nrow(glb_newent_df) == nrow(glb_entity_df))
warning("glb_newent_df same as glb_entity_df")
glb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="cleanse_data",
chunk_step_major=max(glb_script_df$chunk_step_major)+1,
chunk_step_minor=0,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))## chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed import_data 1 0 0.002
## elapsed1 cleanse_data 2 0 3.001Step 2: cleanse data
glb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="inspectORexplore.data",
chunk_step_major=max(glb_script_df$chunk_step_major),
chunk_step_minor=1,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))## chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed1 cleanse_data 2 0 3.001
## elapsed2 inspectORexplore.data 2 1 3.749Step 2.1: inspect/explore data
#print(str(glb_trnent_df))
#View(glb_trnent_df)
# List info gathered for various columns
# <col_name>: <description>; <notes>
# Create new features that help diagnostics
# Create factors of string variables
str_vars <- sapply(1:length(names(glb_trnent_df)),
function(col) ifelse(class(glb_trnent_df[, names(glb_trnent_df)[col]]) == "character",
names(glb_trnent_df)[col], ""))
if (length(str_vars <- setdiff(str_vars[str_vars != ""],
glb_exclude_vars_as_features)) > 0) {
warning("Creating factors of string variables:", paste0(str_vars, collapse=", "))
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, str_vars)
for (var in str_vars) {
glb_entity_df[, paste0(var, ".fctr")] <- factor(glb_entity_df[, var],
as.factor(unique(glb_entity_df[, var])))
glb_trnent_df[, paste0(var, ".fctr")] <- factor(glb_trnent_df[, var],
as.factor(unique(glb_entity_df[, var])))
glb_newent_df[, paste0(var, ".fctr")] <- factor(glb_newent_df[, var],
as.factor(unique(glb_entity_df[, var])))
}
}
# Convert factors to dummy variables
# Build splines require(splines); bsBasis <- bs(training$age, df=3)
add_new_diag_feats <- function(obs_df, ref_df=glb_entity_df) {
require(plyr)
obs_df <- mutate(obs_df,
# <col_name>.NA=is.na(<col_name>),
# <col_name>.fctr=factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# <col_name>.fctr=relevel(factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# "<ref_val>"),
# <col2_name>.fctr=relevel(factor(ifelse(<col1_name> == <val>, "<oth_val>", "<ref_val>")),
# as.factor(c("R", "<ref_val>")),
# ref="<ref_val>"),
# This doesn't work - use sapply instead
# <col_name>.fctr_num=grep(<col_name>, levels(<col_name>.fctr)),
#
# Date.my=as.Date(strptime(Date, "%m/%d/%y %H:%M")),
# Year=year(Date.my),
# Month=months(Date.my),
# Weekday=weekdays(Date.my)
# <col_name>.log=log(<col.name>),
# <col_name>=<table>[as.character(<col2_name>)],
# <col_name>=as.numeric(<col2_name>),
.rnorm=rnorm(n=nrow(obs_df))
)
# If levels of a factor are different across obs_df & glb_newent_df; predict.glm fails
# Transformations not handled by mutate
# obs_df$<col_name>.fctr.num <- sapply(1:nrow(obs_df),
# function(row_ix) grep(obs_df[row_ix, "<col_name>"],
# levels(obs_df[row_ix, "<col_name>.fctr"])))
print(summary(obs_df))
print(sapply(names(obs_df), function(col) sum(is.na(obs_df[, col]))))
return(obs_df)
}
glb_entity_df <- add_new_diag_feats(glb_entity_df)## Loading required package: plyr## email responsive .rnorm
## Length:855 Min. :0.0000 Min. :-3.34531
## Class :character 1st Qu.:0.0000 1st Qu.:-0.67931
## Mode :character Median :0.0000 Median : 0.01604
## Mean :0.1626 Mean :-0.02616
## 3rd Qu.:0.0000 3rd Qu.: 0.65512
## Max. :1.0000 Max. : 3.14435
## email responsive .rnorm
## 0 0 0glb_trnent_df <- add_new_diag_feats(glb_trnent_df)## email responsive .rnorm
## Length:598 Min. :0.0000 Min. :-2.65045
## Class :character 1st Qu.:0.0000 1st Qu.:-0.59923
## Mode :character Median :0.0000 Median : 0.08599
## Mean :0.1622 Mean : 0.09582
## 3rd Qu.:0.0000 3rd Qu.: 0.76313
## Max. :1.0000 Max. : 3.61407
## email responsive .rnorm
## 0 0 0glb_newent_df <- add_new_diag_feats(glb_newent_df)## email responsive .rnorm
## Length:257 Min. :0.0000 Min. :-3.0871
## Class :character 1st Qu.:0.0000 1st Qu.:-0.5451
## Mode :character Median :0.0000 Median : 0.2200
## Mean :0.1634 Mean : 0.1470
## 3rd Qu.:0.0000 3rd Qu.: 0.7998
## Max. :1.0000 Max. : 3.0189
## email responsive .rnorm
## 0 0 0# Histogram of predictor in glb_trnent_df & glb_newent_df
plot_df <- rbind(cbind(glb_trnent_df[, glb_rsp_var_raw, FALSE], data.frame(.data="Training")),
cbind(glb_trnent_df[, glb_rsp_var_raw, FALSE], data.frame(.data="New")))
print(myplot_histogram(plot_df, glb_rsp_var_raw) + facet_wrap(~ .data))## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
# used later in encode.retype.data chunk
glb_display_class_dstrb <- function(var) {
plot_df <- rbind(cbind(glb_trnent_df[, var, FALSE],
data.frame(.data="Training")),
cbind(glb_trnent_df[, var, FALSE],
data.frame(.data="New")))
xtab_df <- mycreate_xtab(plot_df, c(".data", var))
rownames(xtab_df) <- xtab_df$.data
xtab_df <- subset(xtab_df, select=-.data)
print(xtab_df / rowSums(xtab_df))
}
if (glb_is_classification) glb_display_class_dstrb(glb_rsp_var_raw)## Loading required package: reshape2## responsive.0 responsive.1
## New 0.8377926 0.1622074
## Training 0.8377926 0.1622074# Check for duplicates in glb_id_vars
if (length(glb_id_vars) > 0) {
id_vars_dups_df <- subset(id_vars_df <-
mycreate_tbl_df(glb_entity_df[, glb_id_vars, FALSE], glb_id_vars),
.freq > 1)
} else {
tmp_entity_df <- glb_entity_df
tmp_entity_df$.rownames <- rownames(tmp_entity_df)
id_vars_dups_df <- subset(id_vars_df <-
mycreate_tbl_df(tmp_entity_df[, ".rownames", FALSE], ".rownames"),
.freq > 1)
}
if (nrow(id_vars_dups_df) > 0) {
warning("Duplicates found in glb_id_vars data:", nrow(id_vars_dups_df))
myprint_df(id_vars_dups_df)
} else {
# glb_id_vars are unique across obs in both glb_<>_df
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_id_vars)
}
#pairs(subset(glb_trnent_df, select=-c(col_symbol)))
# Check for glb_newent_df & glb_trnent_df features range mismatches
# Other diagnostics:
# print(subset(glb_trnent_df, <col1_name> == max(glb_trnent_df$<col1_name>, na.rm=TRUE) &
# <col2_name> <= mean(glb_trnent_df$<col1_name>, na.rm=TRUE)))
# print(glb_trnent_df[which.max(glb_trnent_df$<col_name>),])
# print(<col_name>_freq_glb_trnent_df <- mycreate_tbl_df(glb_trnent_df, "<col_name>"))
# print(which.min(table(glb_trnent_df$<col_name>)))
# print(which.max(table(glb_trnent_df$<col_name>)))
# print(which.max(table(glb_trnent_df$<col1_name>, glb_trnent_df$<col2_name>)[, 2]))
# print(table(glb_trnent_df$<col1_name>, glb_trnent_df$<col2_name>))
# print(table(is.na(glb_trnent_df$<col1_name>), glb_trnent_df$<col2_name>))
# print(table(sign(glb_trnent_df$<col1_name>), glb_trnent_df$<col2_name>))
# print(mycreate_xtab(glb_trnent_df, <col1_name>))
# print(mycreate_xtab(glb_trnent_df, c(<col1_name>, <col2_name>)))
# print(<col1_name>_<col2_name>_xtab_glb_trnent_df <-
# mycreate_xtab(glb_trnent_df, c("<col1_name>", "<col2_name>")))
# <col1_name>_<col2_name>_xtab_glb_trnent_df[is.na(<col1_name>_<col2_name>_xtab_glb_trnent_df)] <- 0
# print(<col1_name>_<col2_name>_xtab_glb_trnent_df <-
# mutate(<col1_name>_<col2_name>_xtab_glb_trnent_df,
# <col3_name>=(<col1_name> * 1.0) / (<col1_name> + <col2_name>)))
# print(<col2_name>_min_entity_arr <-
# sort(tapply(glb_trnent_df$<col1_name>, glb_trnent_df$<col2_name>, min, na.rm=TRUE)))
# print(<col1_name>_na_by_<col2_name>_arr <-
# sort(tapply(glb_trnent_df$<col1_name>.NA, glb_trnent_df$<col2_name>, mean, na.rm=TRUE)))
# Other plots:
# print(myplot_box(df=glb_trnent_df, ycol_names="<col1_name>"))
# print(myplot_box(df=glb_trnent_df, ycol_names="<col1_name>", xcol_name="<col2_name>"))
# print(myplot_line(subset(glb_trnent_df, Symbol %in% c("KO", "PG")),
# "Date.my", "StockPrice", facet_row_colnames="Symbol") +
# geom_vline(xintercept=as.numeric(as.Date("2003-03-01"))) +
# geom_vline(xintercept=as.numeric(as.Date("1983-01-01")))
# )
# print(myplot_scatter(glb_entity_df, "<col1_name>", "<col2_name>", smooth=TRUE))
# print(myplot_scatter(glb_entity_df, "<col1_name>", "<col2_name>", colorcol_name="<Pred.fctr>") +
# geom_point(data=subset(glb_entity_df, <condition>),
# mapping=aes(x=<x_var>, y=<y_var>), color="red", shape=4, size=5))
glb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="manage_missing_data",
chunk_step_major=max(glb_script_df$chunk_step_major),
chunk_step_minor=glb_script_df[nrow(glb_script_df), "chunk_step_minor"]+1,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))## chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed2 inspectORexplore.data 2 1 3.749
## elapsed3 manage_missing_data 2 2 4.941Step 2.2: manage missing data
# print(sapply(names(glb_trnent_df), function(col) sum(is.na(glb_trnent_df[, col]))))
# print(sapply(names(glb_newent_df), function(col) sum(is.na(glb_newent_df[, col]))))
# glb_trnent_df <- na.omit(glb_trnent_df)
# glb_newent_df <- na.omit(glb_newent_df)
# df[is.na(df)] <- 0
# Not refactored into mydsutils.R since glb_*_df might be reassigned
glb_impute_missing_data <- function(entity_df, newent_df) {
if (!glb_is_separate_newent_dataset) {
# Combine entity & newent
union_df <- rbind(mutate(entity_df, .src = "entity"),
mutate(newent_df, .src = "newent"))
union_imputed_df <- union_df[, setdiff(setdiff(names(entity_df),
glb_rsp_var),
glb_exclude_vars_as_features)]
print(summary(union_imputed_df))
require(mice)
set.seed(glb_mice_complete.seed)
union_imputed_df <- complete(mice(union_imputed_df))
print(summary(union_imputed_df))
union_df[, names(union_imputed_df)] <- union_imputed_df[, names(union_imputed_df)]
print(summary(union_df))
# union_df$.rownames <- rownames(union_df)
# union_df <- orderBy(~.rownames, union_df)
#
# imp_entity_df <- myimport_data(
# url="<imputed_trnng_url>",
# comment="imp_entity_df", force_header=TRUE, print_diagn=TRUE)
# print(all.equal(subset(union_df, select=-c(.src, .rownames, .rnorm)),
# imp_entity_df))
# Partition again
glb_trnent_df <<- subset(union_df, .src == "entity", select=-c(.src, .rownames))
comment(glb_trnent_df) <- "entity_df"
glb_newent_df <<- subset(union_df, .src == "newent", select=-c(.src, .rownames))
comment(glb_newent_df) <- "newent_df"
# Generate summaries
print(summary(entity_df))
print(sapply(names(entity_df), function(col) sum(is.na(entity_df[, col]))))
print(summary(newent_df))
print(sapply(names(newent_df), function(col) sum(is.na(newent_df[, col]))))
} else stop("Not implemented yet")
}
if (glb_impute_na_data) {
if ((sum(sapply(names(glb_trnent_df),
function(col) sum(is.na(glb_trnent_df[, col])))) > 0) |
(sum(sapply(names(glb_newent_df),
function(col) sum(is.na(glb_newent_df[, col])))) > 0))
glb_impute_missing_data(glb_trnent_df, glb_newent_df)
}
glb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="encodeORretype.data",
chunk_step_major=max(glb_script_df$chunk_step_major),
chunk_step_minor=glb_script_df[nrow(glb_script_df), "chunk_step_minor"]+1,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))## chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed3 manage_missing_data 2 2 4.941
## elapsed4 encodeORretype.data 2 3 5.265Step 2.3: encode/retype data
# map_<col_name>_df <- myimport_data(
# url="<map_url>",
# comment="map_<col_name>_df", print_diagn=TRUE)
# map_<col_name>_df <- read.csv(paste0(getwd(), "/data/<file_name>.csv"), strip.white=TRUE)
# glb_trnent_df <- mymap_codes(glb_trnent_df, "<from_col_name>", "<to_col_name>",
# map_<to_col_name>_df, map_join_col_name="<map_join_col_name>",
# map_tgt_col_name="<to_col_name>")
# glb_newent_df <- mymap_codes(glb_newent_df, "<from_col_name>", "<to_col_name>",
# map_<to_col_name>_df, map_join_col_name="<map_join_col_name>",
# map_tgt_col_name="<to_col_name>")
# glb_trnent_df$<col_name>.fctr <- factor(glb_trnent_df$<col_name>,
# as.factor(union(glb_trnent_df$<col_name>, glb_newent_df$<col_name>)))
# glb_newent_df$<col_name>.fctr <- factor(glb_newent_df$<col_name>,
# as.factor(union(glb_trnent_df$<col_name>, glb_newent_df$<col_name>)))
if (!is.null(glb_map_rsp_raw_to_var)) {
glb_entity_df[, glb_rsp_var] <-
glb_map_rsp_raw_to_var(glb_entity_df[, glb_rsp_var_raw])
mycheck_map_results(mapd_df=glb_entity_df,
from_col_name=glb_rsp_var_raw, to_col_name=glb_rsp_var)
glb_trnent_df[, glb_rsp_var] <-
glb_map_rsp_raw_to_var(glb_trnent_df[, glb_rsp_var_raw])
mycheck_map_results(mapd_df=glb_trnent_df,
from_col_name=glb_rsp_var_raw, to_col_name=glb_rsp_var)
glb_newent_df[, glb_rsp_var] <-
glb_map_rsp_raw_to_var(glb_newent_df[, glb_rsp_var_raw])
mycheck_map_results(mapd_df=glb_newent_df,
from_col_name=glb_rsp_var_raw, to_col_name=glb_rsp_var)
if (glb_is_classification) glb_display_class_dstrb(glb_rsp_var)
}## Loading required package: sqldf
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk## responsive responsive.fctr .n
## 1 0 N 716
## 2 1 Y 139
## responsive responsive.fctr .n
## 1 0 N 501
## 2 1 Y 97
## responsive responsive.fctr .n
## 1 0 N 215
## 2 1 Y 42
## responsive.fctr.N responsive.fctr.Y
## New 0.8377926 0.1622074
## Training 0.8377926 0.1622074glb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="extract_features",
chunk_step_major=max(glb_script_df$chunk_step_major)+1,
chunk_step_minor=0,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))## chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed4 encodeORretype.data 2 3 5.265
## elapsed5 extract_features 3 0 8.865Step 3: extract features
#```{r extract_features, cache=FALSE, eval=glb_is_textual}
# Create new features that help prediction
# <col_name>.lag.2 <- lag(zoo(glb_trnent_df$<col_name>), -2, na.pad=TRUE)
# glb_trnent_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
# <col_name>.lag.2 <- lag(zoo(glb_newent_df$<col_name>), -2, na.pad=TRUE)
# glb_newent_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
#
# glb_newent_df[1, "<col_name>.lag.2"] <- glb_trnent_df[nrow(glb_trnent_df) - 1,
# "<col_name>"]
# glb_newent_df[2, "<col_name>.lag.2"] <- glb_trnent_df[nrow(glb_trnent_df),
# "<col_name>"]
# glb_trnent_df <- mutate(glb_trnent_df,
# <new_col_name>=
# )
# glb_newent_df <- mutate(glb_newent_df,
# <new_col_name>=
# )
if (glb_is_textual) {
require(tm)
glb_corpus <- Corpus(VectorSource(glb_entity_df[, glb_txt_var]))
glb_corpus <- tm_map(glb_corpus, tolower)
glb_corpus <- tm_map(glb_corpus, PlainTextDocument)
glb_corpus <- tm_map(glb_corpus, removePunctuation)
glb_corpus <- tm_map(glb_corpus, removeWords,
c(glb_append_stop_words, stopwords("english")))
glb_corpus <- tm_map(glb_corpus, stemDocument)
full_freqs_DTM <- DocumentTermMatrix(glb_corpus)
full_freqs_vctr <- colSums(as.matrix(full_freqs_DTM))
names(full_freqs_vctr) <- dimnames(full_freqs_DTM)[[2]]
full_freqs_df <- as.data.frame(full_freqs_vctr)
names(full_freqs_df) <- "freq.full"
full_freqs_df$term <- rownames(full_freqs_df)
full_freqs_df <- orderBy(~ -freq.full, full_freqs_df)
# ggplot(full_freqs_df, aes(x=freq.full)) + stat_ecdf()
# print(myplot_hbar(head(full_freqs_df, 10), "term", "freq.full"))
sprs_freqs_DTM <- removeSparseTerms(full_freqs_DTM, glb_sprs_threshold)
sprs_freqs_vctr <- colSums(as.matrix(sprs_freqs_DTM))
names(sprs_freqs_vctr) <- dimnames(sprs_freqs_DTM)[[2]]
sprs_freqs_df <- as.data.frame(sprs_freqs_vctr)
names(sprs_freqs_df) <- "freq.sprs"
sprs_freqs_df$term <- rownames(sprs_freqs_df)
sprs_freqs_df <- orderBy(~ -freq.sprs, sprs_freqs_df)
# ggplot(sprs_freqs_df, aes(x=freq.sprs)) + stat_ecdf()
# print(myplot_hbar(head(sprs_freqs_df, 10), "term", "freq.sprs"))
terms_freqs_df <- merge(full_freqs_df, sprs_freqs_df, all.x=TRUE)
melt_freqs_df <- orderBy(~ -value, melt(terms_freqs_df, id.var="term"))
print(ggplot(melt_freqs_df, aes(value, color=variable)) + stat_ecdf() +
geom_hline(yintercept=glb_sprs_threshold, linetype = "dotted"))
print(myplot_hbar(head(melt_freqs_df, 20), "term", "value", colorcol_name="variable"))
melt_freqs_df <- orderBy(~ -value, melt(subset(terms_freqs_df, is.na(freq.sprs)), id.var="term"))
print(myplot_hbar(head(melt_freqs_df, 10), "term", "value", colorcol_name="variable"))
# Create txt features
txt_X_df <- as.data.frame(as.matrix(sprs_freqs_DTM))
colnames(txt_X_df) <- make.names(colnames(txt_X_df))
# Add dependent variable
glb_entity_df <- cbind(glb_entity_df, txt_X_df)
# a working copy of this is reqd in manage.missingdata chunk
union_df <- rbind(mutate(glb_trnent_df, .src = "trnent"),
mutate(glb_newent_df, .src = "newent"))
if (is.null(glb_id_vars)) {
union_df$.rownames <- rownames(union_df)
tmp_entity_df <- glb_entity_df
tmp_entity_df$.rownames <- rownames(tmp_entity_df)
mrg_entity_df <- merge(tmp_entity_df, union_df[, c(".src", ".rownames")])
} else stop("not implemented yet")
# Partition again
glb_trnent_df <- subset(mrg_entity_df, .src == "trnent", select=-c(.src, .rownames))
comment(glb_trnent_df) <- "trnent_df"
glb_newent_df <- subset(mrg_entity_df, .src == "newent", select=-c(.src, .rownames))
comment(glb_newent_df) <- "newent_df"
# Generate summaries
print(summary(glb_entity_df))
print(sapply(names(glb_entity_df), function(col) sum(is.na(glb_entity_df[, col]))))
print(summary(glb_trnent_df))
print(sapply(names(glb_trnent_df), function(col) sum(is.na(glb_trnent_df[, col]))))
print(summary(glb_newent_df))
print(sapply(names(glb_newent_df), function(col) sum(is.na(glb_newent_df[, col]))))
}## Loading required package: tm
## Loading required package: NLP
##
## Attaching package: 'NLP'
##
## The following object is masked from 'package:ggplot2':
##
## annotate## Warning: Removed 6 rows containing missing values (geom_path).
## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
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## --> row.names NOT used## Warning in merge.data.frame(tmp_entity_df, union_df[, c(".src",
## ".rownames")]): column name 'email' is duplicated in the result
## email responsive .rnorm responsive.fctr
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## Class :character 1st Qu.:0.0000 1st Qu.:-0.67931 Y:139
## Mode :character Median :0.0000 Median : 0.01604
## Mean :0.1626 Mean :-0.02616
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## Max. :1.0000 Max. : 3.14435
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## Median :0.00000 Median :0.00000 Median : 0.00000 Median : 0.000
## Mean :0.09591 Mean :0.05965 Mean : 0.08889 Mean : 0.414
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## Max. :5.00000 Max. :5.00000 Max. :12.00000 Max. :36.000
## X2001 X713 X77002 abl
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## Median :0.0000 Median : 0.0000 Median : 0.0000 Median :0.0000
## Mean :0.0924 Mean : 0.1357 Mean : 0.1404 Mean :0.1216
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## Median :0.00000 Median : 0.00000 Median :0.00000 Median : 0.0000
## Mean :0.03509 Mean : 0.07368 Mean :0.07018 Mean : 0.1357
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## Median :0.0000 Median : 0.0000 Median :0.00000 Median :0.00000
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## Median :0.0000 Median :0.00000 Median : 0.00000 Median : 0.0000
## Mean :0.1099 Mean :0.08772 Mean : 0.08772 Mean : 0.1778
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## Median : 0.0000 Median :0.0000 Median :0.0000 Median :0.00000
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## charg check chris citi
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## Median : 0.0000 Median :0.00000 Median :0.000 Median : 0.00000
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## 0 0 0
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## peak peopl per
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## percent perform period
## 0 0 0
## person peter pge
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## phone physic pipelin
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## place plan plant
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## portion posit possibl
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## profit program prohibit
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## project propos protect
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## rate rather reach
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## read readi real
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## transfer transmiss transport
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## 0 0 0
## word work year
## 0 0 0
## yesterday yet york
## 0 0 0# print(sapply(names(glb_trnent_df), function(col) sum(is.na(glb_trnent_df[, col]))))
# print(sapply(names(glb_newent_df), function(col) sum(is.na(glb_newent_df[, col]))))
# print(myplot_scatter(glb_trnent_df, "<col1_name>", "<col2_name>", smooth=TRUE))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all","data.new")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
glb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="select_features",
chunk_step_major=max(glb_script_df$chunk_step_major)+1,
chunk_step_minor=0,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))## chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed5 extract_features 3 0 8.865
## elapsed6 select_features 4 0 22.648Step 4: select features
print(glb_feats_df <- myselect_features(entity_df=glb_trnent_df,
exclude_vars_as_features=glb_exclude_vars_as_features,
rsp_var=glb_rsp_var))## id cor.y exclude.as.feat
## responsive responsive 1.0000000000 1
## price price 0.3618825045 0
## independ independ 0.3396874146 0
## demand demand 0.3297669856 0
## generat generat 0.3176296544 0
## suppli suppli 0.3117929815 0
## emerg emerg 0.3067129504 0
## summer summer 0.3052782353 0
## ferc ferc 0.3038831190 0
## southern southern 0.3030500292 0
## util util 0.3013472796 0
## custom custom 0.2940276360 0
## dow dow 0.2905452391 0
## high high 0.2878089610 0
## oper oper 0.2864082863 0
## california california 0.2846784134 0
## transmiss transmiss 0.2839337984 0
## natur natur 0.2831368206 0
## power power 0.2827140397 0
## iso iso 0.2818408999 0
## jone jone 0.2725080864 0
## forc forc 0.2709724176 0
## gas gas 0.2687789239 0
## longer longer 0.2684070550 0
## capac capac 0.2682464816 0
## low low 0.2638271167 0
## increas increas 0.2633593653 0
## expect expect 0.2632412709 0
## problem problem 0.2619523262 0
## consum consum 0.2613087272 0
## pay pay 0.2559409694 0
## system system 0.2555675922 0
## plant plant 0.2546623146 0
## take take 0.2533694872 0
## higher higher 0.2521854541 0
## rate rate 0.2518307666 0
## feder feder 0.2514381618 0
## said said 0.2510175191 0
## continu continu 0.2508018743 0
## cap cap 0.2503576934 0
## commiss commiss 0.2476310885 0
## sourc sourc 0.2475682961 0
## solut solut 0.2468279601 0
## three three 0.2456137740 0
## among among 0.2446311268 0
## right right 0.2433876522 0
## addit addit 0.2427842871 0
## order order 0.2420327381 0
## author author 0.2409912244 0
## line line 0.2406086636 0
## energi energi 0.2393082994 0
## bring bring 0.2377341515 0
## push push 0.2357325995 0
## major major 0.2348211062 0
## load load 0.2344112074 0
## told told 0.2338483879 0
## can can 0.2326877882 0
## lower lower 0.2325942368 0
## public public 0.2325695590 0
## pipelin pipelin 0.2308550156 0
## peak peak 0.2299243697 0
## cost cost 0.2299010666 0
## supplier supplier 0.2293560637 0
## without without 0.2274353798 0
## electr electr 0.2274094320 0
## state state 0.2270107697 0
## copyright copyright 0.2262725143 0
## hour hour 0.2258881509 0
## interconnect interconnect 0.2255808741 0
## econom econom 0.2254009118 0
## two two 0.2252231194 0
## implement implement 0.2250690523 0
## analyst analyst 0.2250645423 0
## june june 0.2249803607 0
## million million 0.2246542151 0
## level level 0.2244030930 0
## close close 0.2232701830 0
## establish establish 0.2224896558 0
## want want 0.2212168703 0
## within within 0.2207546618 0
## purchas purchas 0.2206530396 0
## other other 0.2205792725 0
## part part 0.2196076232 0
## announc announc 0.2182869183 0
## enough enough 0.2166223912 0
## servic servic 0.2163977535 0
## market market 0.2161624304 0
## inc inc 0.2158866148 0
## grid grid 0.2155573951 0
## provid provid 0.2149103660 0
## competit competit 0.2143873820 0
## reduc reduc 0.2141014660 0
## regulatori regulatori 0.2132046651 0
## news news 0.2123104586 0
## steffesnaenronenron steffesnaenronenron 0.2117758013 0
## creat creat 0.2111880091 0
## propos propos 0.2106227221 0
## still still 0.2105777407 0
## situat situat 0.2100919577 0
## profit profit 0.2099004147 0
## X2000 X2000 0.2096603161 0
## whether whether 0.2088772077 0
## wholesal wholesal 0.2086657810 0
## transport transport 0.2083503333 0
## paid paid 0.2083017839 0
## fall fall 0.2081523884 0
## action action 0.2079681852 0
## serv serv 0.2065072719 0
## rais rais 0.2063371449 0
## fuel fuel 0.2061286981 0
## technolog technolog 0.2060830653 0
## avail avail 0.2056318732 0
## will will 0.2049145694 0
## today today 0.2041844132 0
## full full 0.2032383377 0
## unit unit 0.2026001708 0
## face face 0.2022985948 0
## pass pass 0.2022474204 0
## compani compani 0.2015851458 0
## requir requir 0.2013891297 0
## cut cut 0.2009874514 0
## point point 0.2003566548 0
## step step 0.1989482878 0
## even even 0.1987643062 0
## andor andor 0.1983043848 0
## earlier earlier 0.1971131364 0
## design design 0.1969664065 0
## past past 0.1969126051 0
## result result 0.1966880711 0
## pacif pacif 0.1962100354 0
## sell sell 0.1957693699 0
## mani mani 0.1957379262 0
## reason reason 0.1956037205 0
## also also 0.1949052432 0
## reserv reserv 0.1944975982 0
## one one 0.1944280715 0
## nation nation 0.1941781857 0
## time time 0.1940853989 0
## consid consid 0.1938138386 0
## billion billion 0.1938001528 0
## percent percent 0.1931532753 0
## deregul deregul 0.1926780215 0
## buy buy 0.1925542965 0
## industri industri 0.1924104543 0
## money money 0.1923161987 0
## key key 0.1919339699 0
## presid presid 0.1902098608 0
## day day 0.1900533742 0
## set set 0.1897918321 0
## remain remain 0.1897793429 0
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## success success 0.1892794931 0
## plan plan 0.1888015922 0
## edison edison 0.1886458114 0
## call call 0.1879236680 0
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## own own 0.1863898334 0
## earli earli 0.1860802434 0
## spot spot 0.1859262602 0
## includ includ 0.1853247428 0
## year year 0.1848517185 0
## appear appear 0.1847178563 0
## bid bid 0.1838604673 0
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## deliveri deliveri 0.1815451261 0
## sinc sinc 0.1809161655 0
## got got 0.1808899624 0
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## now now 0.1798495804 0
## resourc resourc 0.1781253525 0
## jeff jeff 0.1781021390 0
## small small 0.1776504970 0
## articl articl 0.1773456393 0
## charg charg 0.1772441050 0
## per per 0.1769463305 0
## washington washington 0.1762382161 0
## dollar dollar 0.1760740085 0
## better better 0.1750624183 0
## seller seller 0.1750178911 0
## contract contract 0.1746950048 0
## benefit benefit 0.1744341198 0
## board board 0.1743414318 0
## larg larg 0.1739541420 0
## wednesday wednesday 0.1738380925 0
## help help 0.1738227174 0
## near near 0.1735284019 0
## sold sold 0.1733073064 0
## term term 0.1732181556 0
## turn turn 0.1729140993 0
## construct construct 0.1723352395 0
## five five 0.1718747926 0
## much much 0.1715659442 0
## like like 0.1712684417 0
## around around 0.1710622811 0
## amount amount 0.1708830127 0
## direct direct 0.1708063710 0
## period period 0.1705841762 0
## top top 0.1704771642 0
## structur structur 0.1704408155 0
## offici offici 0.1698075411 0
## governor governor 0.1689695766 0
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## meet meet 0.1681089925 0
## rather rather 0.1675197494 0
## end end 0.1670527701 0
## friday friday 0.1670043182 0
## report report 0.1669395811 0
## may may 0.1668431434 0
## fact fact 0.1666400751 0
## dont dont 0.1665355866 0
## agenc agenc 0.1663038669 0
## offer offer 0.1661305622 0
## use use 0.1659611198 0
## follow follow 0.1658470501 0
## file file 0.1655333279 0
## taken taken 0.1651649324 0
## purpos purpos 0.1649856337 0
## opinion opinion 0.1646195308 0
## talk talk 0.1644530107 0
## certain certain 0.1642331018 0
## grow grow 0.1642259544 0
## good good 0.1639940852 0
## keep keep 0.1639297703 0
## altern altern 0.1635639893 0
## busi busi 0.1618432907 0
## dasovichnaenron dasovichnaenron 0.1614242938 0
## exist exist 0.1614063800 0
## come come 0.1613047724 0
## make make 0.1612484845 0
## real real 0.1606075191 0
## drive drive 0.1602984764 0
## avoid avoid 0.1594811790 0
## pge pge 0.1592319589 0
## monday monday 0.1590398916 0
## effort effort 0.1582847606 0
## effici effici 0.1580716521 0
## month month 0.1578979258 0
## back back 0.1577318831 0
## repres repres 0.1575593900 0
## measur measur 0.1569021120 0
## davi davi 0.1562381205 0
## determin determin 0.1559586937 0
## exchang exchang 0.1558135067 0
## develop develop 0.1558093848 0
## lead lead 0.1553384185 0
## need need 0.1551813033 0
## januari januari 0.1533639023 0
## payment payment 0.1530879864 0
## caus caus 0.1530010652 0
## immedi immedi 0.1529486160 0
## least least 0.1529437010 0
## run run 0.1529318886 0
## explain explain 0.1523945327 0
## messag messag 0.1523859979 0
## havent havent 0.1519305599 0
## next. next. 0.1515389124 0
## tuesday tuesday 0.1514715368 0
## occur occur 0.1512442259 0
## san san 0.1511090248 0
## receiv receiv 0.1509909335 0
## program program 0.1502598844 0
## upon upon 0.1502436740 0
## start start 0.1501561331 0
## refer refer 0.1495907002 0
## ago ago 0.1494947820 0
## prohibit prohibit 0.1486384902 0
## approxim approxim 0.1485690318 0
## although although 0.1485232829 0
## differ differ 0.1481531680 0
## releas releas 0.1478437154 0
## legisl legisl 0.1476512257 0
## analysi analysi 0.1471947144 0
## just just 0.1470908222 0
## later later 0.1457326690 0
## open open 0.1454156000 0
## chairman chairman 0.1453714591 0
## region region 0.1451674396 0
## move move 0.1442599277 0
## clear clear 0.1438512387 0
## administr administr 0.1436463483 0
## forward forward 0.1436213223 0
## post post 0.1428155975 0
## director director 0.1424163619 0
## decemb decemb 0.1423802660 0
## produc produc 0.1423620804 0
## believ believ 0.1420843002 0
## estim estim 0.1410526561 0
## alreadi alreadi 0.1410233595 0
## fix fix 0.1409112671 0
## found found 0.1404394483 0
## recent recent 0.1402148082 0
## tri tri 0.1400410948 0
## consult consult 0.1399908689 0
## facil facil 0.1399130621 0
## particip particip 0.1396212581 0
## trader trader 0.1391721067 0
## seek seek 0.1391416209 0
## see see 0.1388929384 0
## novemb novemb 0.1386029352 0
## crisi crisi 0.1384501258 0
## cpuc cpuc 0.1381157700 0
## richard richard 0.1371399699 0
## far far 0.1364821085 0
## entir entir 0.1362819129 0
## investig investig 0.1359203905 0
## polici polici 0.1357848747 0
## retail retail 0.1354326032 0
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## free free 0.1351955995 0
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## respons respons 0.1346671214 0
## materi materi 0.1346078293 0
## modifi modifi 0.1344646852 0
## X100 X100 0.1338898408 0
## citi citi 0.1336268117 0
## X2001 X2001 0.1332320934 0
## steven steven 0.1331759332 0
## new new 0.1328962348 0
## third third 0.1325701751 0
## cover cover 0.1323380010 0
## oblig oblig 0.1321929675 0
## long long 0.1321461714 0
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## august august 0.1310950534 0
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## return return 0.1301874168 0
## possibl possibl 0.1300964353 0
## tariff tariff 0.1298624940 0
## procedur procedur 0.1297346815 0
## might might 0.1294509130 0
## person person 0.1293200426 0
## regul regul 0.1291806308 0
## readi readi 0.1277559640 0
## condit condit 0.1277129624 0
## agre agre 0.1276678428 0
## though though 0.1273949815 0
## due due 0.1273219088 0
## four four 0.1273147841 0
## combin combin 0.1272186050 0
## X1999 X1999 0.1271507520 0
## big big 0.1270473542 0
## someth someth 0.1269807767 0
## comput comput 0.1266325207 0
## look look 0.1266323850 0
## averag averag 0.1264408658 0
## replac replac 0.1261817548 0
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## particular particular 0.1247164627 0
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## decis decis 0.1243710838 0
## becom becom 0.1242550168 0
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## connect connect 0.1237597425 0
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## posit posit 0.1229413931 0
## thing thing 0.1225618772 0
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## potenti potenti 0.1213145822 0
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## thursday thursday 0.1203887424 0
## made made 0.1201537317 0
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## given given 0.1107313055 0
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## that that 0.1102494341 0
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## X1999 0.1271507520
## big 0.1270473542
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## say 0.1070085405
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## reach 0.1057967130
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## rule 0.1031834028
## guarante 0.1029279091
## address 0.1023573041
## govern 0.1021681548
## associ 0.1014997755
## practic 0.1014693972
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## access 0.1001766721
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## identifi 0.0972628428
## internet 0.0970458010
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## express 0.0965598324
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## general 0.0961287787
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## qualiti 0.0951497585
## confidenti 0.0945909102
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## show 0.0940832934
## comment 0.0939611357
## peopl 0.0939593647
## eric 0.0937088010
## correct 0.0936543483
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## similar 0.0926288218
## assum 0.0923432579
## fund 0.0922134751
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## sign 0.0917946026
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## cash 0.0904710607
## option 0.0903774814
## tell 0.0902085634
## add 0.0896725857
## word 0.0895726579
## commerci 0.0894652593
## flow 0.0890353285
## sale 0.0887618472
## limit 0.0875480051
## attorney 0.0871924819
## previous 0.0871672578
## concern 0.0870677550
## submit 0.0869294443
## karen 0.0866870494
## approach 0.0865847422
## data 0.0865621627
## necessari 0.0856839959
## decid 0.0852910750
## various 0.0850953580
## recommend 0.0846846932
## sara 0.0846721944
## abl 0.0845103278
## along 0.0826894388
## water 0.0822357844
## taylorhouectect 0.0822020727
## physic 0.0819829879
## tana 0.0813834485
## claim 0.0807954025
## final 0.0797774344
## error 0.0794567199
## number 0.0793898710
## event 0.0793807412
## think 0.0793506325
## paul 0.0790723051
## alan 0.0787157208
## april 0.0784422662
## parti 0.0782320138
## exampl 0.0780327450
## els 0.0777880196
## andrew 0.0775156262
## group 0.0774935715
## anyon 0.0774112768
## joneshouectect 0.0771979052
## dissemin 0.0771159461
## distribut 0.0766352136
## onlin 0.0764491763
## thank 0.0759121610
## realli 0.0757745081
## south 0.0748117731
## revis 0.0747080213
## delet 0.0745784299
## improv 0.0740910125
## sue 0.0739783573
## law 0.0739724010
## individu 0.0728187898
## oil 0.0723765217
## prepar 0.0722157759
## hear 0.0721767277
## leav 0.0721767277
## advis 0.0720170955
## head 0.0720143462
## standard 0.0715727310
## cynthia 0.0715587325
## joe 0.0709130462
## notifi 0.0704616996
## speak 0.0704531247
## thought 0.0704497894
## jan 0.0704418453
## tim 0.0696880559
## know 0.0690297309
## awar 0.0681569147
## strong 0.0681569147
## page 0.0676564630
## mean 0.0675706811
## sender 0.0671392874
## activ 0.0658970649
## organ 0.0657253994
## X713 0.0656933788
## east 0.0653380576
## present 0.0651165255
## protect 0.0647883342
## corpor 0.0645460699
## west 0.0641162246
## kevin 0.0633773371
## enron 0.0631394157
## initi 0.0627218535
## singl 0.0627002517
## area 0.0618636151
## X1400 0.0616012359
## relat 0.0615984503
## kind 0.0615829881
## everyon 0.0610081987
## firm 0.0609252997
## book 0.0609142466
## electron 0.0608600186
## extend 0.0607400058
## assist 0.0606951685
## affect 0.0597367710
## octob 0.0592981458
## origin 0.0589947692
## specif 0.0588088195
## attent 0.0584754134
## septemb 0.0582146129
## deliv 0.0576299250
## chang 0.0574315256
## york 0.0571095814
## attach 0.0568613498
## kate 0.0565203038
## rob 0.0562339647
## eol 0.0560604765
## hard 0.0553334463
## north 0.0550875347
## valu 0.0549418577
## termin 0.0548853358
## amend 0.0546989206
## print 0.0541922676
## check 0.0541349233
## capit 0.0538705276
## offic 0.0533867277
## either 0.0533418938
## david 0.0528946310
## calcul 0.0526493593
## yet 0.0525890015
## ill 0.0525197568
## consider 0.0524228791
## side 0.0523334188
## approv 0.0517967139
## X77002 0.0512452278
## request 0.0511905158
## shall 0.0510729732
## asset 0.0506077006
## product 0.0506063802
## feel 0.0505763218
## let 0.0504840325
## carol 0.0502871627
## master 0.0500238766
## someon 0.0499430030
## march 0.0495227570
## probabl 0.0492569277
## sent 0.0491182338
## commit 0.0482834163
## mike 0.0479164286
## languag 0.0471517737
## appropri 0.0467062667
## matter 0.0463762306
## counterparti 0.0461423791
## depend 0.0458701667
## research 0.0458701667
## chris 0.0456306353
## form 0.0455249236
## keannaenronenron 0.0454522047
## quick 0.0453249054
## contain 0.0446346826
## fax 0.0445273304
## hand 0.0444189166
## regard 0.0440726720
## gari 0.0437381626
## respect 0.0434521515
## steve 0.0432869353
## dear 0.0416546021
## guy 0.0410468650
## date 0.0403359306
## memo 0.0403299954
## deal 0.0398798722
## tomorrow 0.0394688320
## repli 0.0394176584
## latest 0.0379740489
## stephani 0.0379425767
## interest 0.0379392344
## extens 0.0371718798
## scott 0.0369975270
## financi 0.0361953951
## liquid 0.0357610192
## smith 0.0355770649
## texa 0.0344897387
## local 0.0341722755
## intern 0.0336551152
## entiti 0.0333968167
## vinc 0.0333314830
## anyth 0.0333126394
## team 0.0329677747
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## great 0.0321235816
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## europ 0.0312969405
## america 0.0310873866
## risk 0.0310867107
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## impact 0.0309266310
## proceed 0.0305939345
## desk 0.0305569005
## transfer 0.0303425388
## info 0.0302835205
## ive 0.0302190505
## done 0.0300713346
## harri 0.0296314217
## llc 0.0294418606
## afternoon 0.0293898025
## mari 0.0292030884
## michael 0.0288993551
## cours 0.0288839391
## best 0.0288031434
## perform 0.0278086157
## link 0.0271660147
## delay 0.0267673198
## account 0.0265246912
## committe 0.0264524308
## opportun 0.0263451975
## privileg 0.0262846494
## process 0.0259214747
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## sure 0.0250212576
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## greg 0.0245976871
## houston 0.0243561924
## discuss 0.0236944927
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## legal 0.0230874863
## .rnorm 0.0227856599
## elizabeth 0.0227235140
## recipi 0.0221176026
## letter 0.0219948551
## volum 0.0217931113
## london 0.0214605816
## transact 0.0211304939
## confirm 0.0209729992
## agreement 0.0209354092
## lisa 0.0205789591
## lesli 0.0204283587
## roger 0.0198464792
## mention 0.0197639506
## jeffrey 0.0194129806
## summari 0.0192597174
## convers 0.0191495933
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## john 0.0188497148
## prior 0.0183740707
## counsel 0.0181877075
## fyi 0.0179059962
## copi 0.0178634241
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## ena 0.0169622682
## respond 0.0169284474
## gerald 0.0168650792
## bit 0.0154817094
## yesterday 0.0152635513
## websit 0.0147506093
## phone 0.0142806549
## format 0.0139567936
## review 0.0136548390
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## paper 0.0124314314
## bank 0.0120254810
## georg 0.0114081588
## peter 0.0112611240
## handl 0.0112502777
## kelli 0.0112502777
## document 0.0108489602
## item 0.0108123276
## employe 0.0101712950
## communic 0.0101152504
## brief 0.0099499211
## rick 0.0099388566
## etc 0.0095535235
## kaminskihouect 0.0091513002
## net 0.0091078217
## understand 0.0087271072
## provis 0.0076033052
## credit 0.0073698818
## februari 0.0069539241
## suit 0.0066289051
## mark 0.0062667388
## sorri 0.0061598368
## appli 0.0059544775
## frank 0.0058346023
## type 0.0056680553
## robert 0.0052736305
## attend 0.0048625691
## strategi 0.0048374936
## version 0.0041245241
## bob 0.0041026714
## dave 0.0037959924
## email.1 0.0034108360
## status 0.0030432078
## street 0.0027954069
## contact 0.0024545949
## draft 0.0021763192
## join 0.0016967513
## dan 0.0010462053
## list 0.0010182563
## appreci 0.0005628301
## met 0.0003681005
## ken 0.0002703781glb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="remove_correlated_features",
chunk_step_major=max(glb_script_df$chunk_step_major),
chunk_step_minor=glb_script_df[nrow(glb_script_df), "chunk_step_minor"]+1,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))## chunk_label chunk_step_major chunk_step_minor
## elapsed6 select_features 4 0
## elapsed7 remove_correlated_features 4 1
## elapsed
## elapsed6 22.648
## elapsed7 24.394Step 4.1: remove correlated features
print(glb_feats_df <- orderBy(~-cor.y,
myfind_cor_features(feats_df=glb_feats_df, entity_df=glb_trnent_df,
rsp_var=glb_rsp_var,
checkConditionalX=(glb_is_classification && glb_is_binomial))))## Loading required package: caret
## Loading required package: lattice
##
## Attaching package: 'caret'
##
## The following object is masked from 'package:survival':
##
## cluster## [1] "cor(dow, jone)=0.9839"
## [1] "cor(responsive.fctr, dow)=0.2905"
## [1] "cor(responsive.fctr, jone)=0.2725"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified jone as highly correlated with dow
## [1] "cor(home, word)=0.9504"
## [1] "cor(responsive.fctr, home)=0.1139"
## [1] "cor(responsive.fctr, word)=0.0896"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified word as highly correlated with home
## [1] "cor(home, water)=0.9460"
## [1] "cor(responsive.fctr, home)=0.1139"
## [1] "cor(responsive.fctr, water)=0.0822"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified water as highly correlated with home
## [1] "cor(act, caus)=0.9448"
## [1] "cor(responsive.fctr, act)=0.1134"
## [1] "cor(responsive.fctr, caus)=0.1530"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified act as highly correlated with caus
## [1] "cor(drive, home)=0.9435"
## [1] "cor(responsive.fctr, drive)=0.1603"
## [1] "cor(responsive.fctr, home)=0.1139"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified home as highly correlated with drive
## [1] "cor(california, electr)=0.9433"
## [1] "cor(responsive.fctr, california)=0.2847"
## [1] "cor(responsive.fctr, electr)=0.2274"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified electr as highly correlated with california
## [1] "cor(billion, state)=0.9385"
## [1] "cor(responsive.fctr, billion)=0.1938"
## [1] "cor(responsive.fctr, state)=0.2270"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified billion as highly correlated with state
## [1] "cor(drive, state)=0.9380"
## [1] "cor(responsive.fctr, drive)=0.1603"
## [1] "cor(responsive.fctr, state)=0.2270"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified drive as highly correlated with state
## [1] "cor(associ, crisi)=0.9378"
## [1] "cor(responsive.fctr, associ)=0.1015"
## [1] "cor(responsive.fctr, crisi)=0.1385"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified associ as highly correlated with crisi
## [1] "cor(power, state)=0.9343"
## [1] "cor(responsive.fctr, power)=0.2827"
## [1] "cor(responsive.fctr, state)=0.2270"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified state as highly correlated with power
## [1] "cor(say, way)=0.9315"
## [1] "cor(responsive.fctr, say)=0.1070"
## [1] "cor(responsive.fctr, way)=0.1251"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified say as highly correlated with way
## [1] "cor(juli, peopl)=0.9272"
## [1] "cor(responsive.fctr, juli)=0.1205"
## [1] "cor(responsive.fctr, peopl)=0.0940"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified peopl as highly correlated with juli
## [1] "cor(crisi, press)=0.9266"
## [1] "cor(responsive.fctr, crisi)=0.1385"
## [1] "cor(responsive.fctr, press)=0.1260"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified press as highly correlated with crisi
## [1] "cor(caus, practic)=0.9220"
## [1] "cor(responsive.fctr, caus)=0.1530"
## [1] "cor(responsive.fctr, practic)=0.1015"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified practic as highly correlated with caus
## [1] "cor(crisi, though)=0.9211"
## [1] "cor(responsive.fctr, crisi)=0.1385"
## [1] "cor(responsive.fctr, though)=0.1274"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified though as highly correlated with crisi
## [1] "cor(deregul, power)=0.9206"
## [1] "cor(responsive.fctr, deregul)=0.1927"
## [1] "cor(responsive.fctr, power)=0.2827"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified deregul as highly correlated with power
## [1] "cor(crisi, offici)=0.9205"
## [1] "cor(responsive.fctr, crisi)=0.1385"
## [1] "cor(responsive.fctr, offici)=0.1698"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified crisi as highly correlated with offici
## [1] "cor(davi, juli)=0.9185"
## [1] "cor(responsive.fctr, davi)=0.1562"
## [1] "cor(responsive.fctr, juli)=0.1205"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified juli as highly correlated with davi
## [1] "cor(caus, wholesal)=0.9068"
## [1] "cor(responsive.fctr, caus)=0.1530"
## [1] "cor(responsive.fctr, wholesal)=0.2087"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified caus as highly correlated with wholesal
## [1] "cor(build, financi)=0.9051"
## [1] "cor(responsive.fctr, build)=0.0990"
## [1] "cor(responsive.fctr, financi)=0.0362"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified financi as highly correlated with build
## [1] "cor(much, run)=0.9023"
## [1] "cor(responsive.fctr, much)=0.1716"
## [1] "cor(responsive.fctr, run)=0.1529"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified run as highly correlated with much
## [1] "cor(action, wholesal)=0.9009"
## [1] "cor(responsive.fctr, action)=0.2080"
## [1] "cor(responsive.fctr, wholesal)=0.2087"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified action as highly correlated with wholesal
## [1] "cor(offici, way)=0.8996"
## [1] "cor(responsive.fctr, offici)=0.1698"
## [1] "cor(responsive.fctr, way)=0.1251"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified way as highly correlated with offici
## [1] "cor(california, consum)=0.8919"
## [1] "cor(responsive.fctr, california)=0.2847"
## [1] "cor(responsive.fctr, consum)=0.2613"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified consum as highly correlated with california
## [1] "cor(davi, offici)=0.8908"
## [1] "cor(responsive.fctr, davi)=0.1562"
## [1] "cor(responsive.fctr, offici)=0.1698"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified davi as highly correlated with offici
## [1] "cor(build, risk)=0.8862"
## [1] "cor(responsive.fctr, build)=0.0990"
## [1] "cor(responsive.fctr, risk)=0.0311"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified risk as highly correlated with build
## [1] "cor(much, offici)=0.8819"
## [1] "cor(responsive.fctr, much)=0.1716"
## [1] "cor(responsive.fctr, offici)=0.1698"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified offici as highly correlated with much
## [1] "cor(busi, california)=0.8764"
## [1] "cor(responsive.fctr, busi)=0.1618"
## [1] "cor(responsive.fctr, california)=0.2847"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified busi as highly correlated with california
## [1] "cor(energi, power)=0.8748"
## [1] "cor(responsive.fctr, energi)=0.2393"
## [1] "cor(responsive.fctr, power)=0.2827"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified energi as highly correlated with power
## [1] "cor(california, southern)=0.8732"
## [1] "cor(responsive.fctr, california)=0.2847"
## [1] "cor(responsive.fctr, southern)=0.3031"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified california as highly correlated with southern
## [1] "cor(even, much)=0.8690"
## [1] "cor(responsive.fctr, even)=0.1988"
## [1] "cor(responsive.fctr, much)=0.1716"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified much as highly correlated with even
## [1] "cor(just, now)=0.8667"
## [1] "cor(responsive.fctr, just)=0.1471"
## [1] "cor(responsive.fctr, now)=0.1798"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified just as highly correlated with now
## [1] "cor(build, new)=0.8654"
## [1] "cor(responsive.fctr, build)=0.0990"
## [1] "cor(responsive.fctr, new)=0.1329"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified build as highly correlated with new
## [1] "cor(power, profit)=0.8645"
## [1] "cor(responsive.fctr, power)=0.2827"
## [1] "cor(responsive.fctr, profit)=0.2099"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified profit as highly correlated with power
## [1] "cor(real, wholesal)=0.8614"
## [1] "cor(responsive.fctr, real)=0.1606"
## [1] "cor(responsive.fctr, wholesal)=0.2087"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified real as highly correlated with wholesal
## [1] "cor(found, power)=0.8589"
## [1] "cor(responsive.fctr, found)=0.1404"
## [1] "cor(responsive.fctr, power)=0.2827"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified found as highly correlated with power
## [1] "cor(buy, near)=0.8587"
## [1] "cor(responsive.fctr, buy)=0.1926"
## [1] "cor(responsive.fctr, near)=0.1735"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified near as highly correlated with buy
## [1] "cor(X1400, X77002)=0.8574"
## [1] "cor(responsive.fctr, X1400)=-0.0616"
## [1] "cor(responsive.fctr, X77002)=-0.0512"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified X77002 as highly correlated with X1400
## [1] "cor(even, power)=0.8514"
## [1] "cor(responsive.fctr, even)=0.1988"
## [1] "cor(responsive.fctr, power)=0.2827"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified even as highly correlated with power
## [1] "cor(competit, wholesal)=0.8501"
## [1] "cor(responsive.fctr, competit)=0.2144"
## [1] "cor(responsive.fctr, wholesal)=0.2087"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified wholesal as highly correlated with competit
## [1] "cor(buy, dont)=0.8500"
## [1] "cor(responsive.fctr, buy)=0.1926"
## [1] "cor(responsive.fctr, dont)=0.1665"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified dont as highly correlated with buy
## [1] "cor(market, trader)=0.8463"
## [1] "cor(responsive.fctr, market)=0.2162"
## [1] "cor(responsive.fctr, trader)=0.1392"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified trader as highly correlated with market
## [1] "cor(copyright, inc)=0.8433"
## [1] "cor(responsive.fctr, copyright)=0.2263"
## [1] "cor(responsive.fctr, inc)=0.2159"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified inc as highly correlated with copyright
## [1] "cor(level, market)=0.8406"
## [1] "cor(responsive.fctr, level)=0.2244"
## [1] "cor(responsive.fctr, market)=0.2162"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified market as highly correlated with level
## [1] "cor(new, york)=0.8405"
## [1] "cor(responsive.fctr, new)=0.1329"
## [1] "cor(responsive.fctr, york)=0.0571"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified york as highly correlated with new
## [1] "cor(begin, time)=0.8397"
## [1] "cor(responsive.fctr, begin)=0.1185"
## [1] "cor(responsive.fctr, time)=0.1941"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified begin as highly correlated with time
## [1] "cor(everi, time)=0.8385"
## [1] "cor(responsive.fctr, everi)=0.1260"
## [1] "cor(responsive.fctr, time)=0.1941"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified everi as highly correlated with time
## [1] "cor(power, two)=0.8384"
## [1] "cor(responsive.fctr, power)=0.2827"
## [1] "cor(responsive.fctr, two)=0.2252"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified two as highly correlated with power
## [1] "cor(less, privat)=0.8371"
## [1] "cor(responsive.fctr, less)=0.1056"
## [1] "cor(responsive.fctr, privat)=0.1119"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified less as highly correlated with privat
## [1] "cor(result, sold)=0.8367"
## [1] "cor(responsive.fctr, result)=0.1967"
## [1] "cor(responsive.fctr, sold)=0.1733"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified sold as highly correlated with result
## [1] "cor(law, use)=0.8333"
## [1] "cor(responsive.fctr, law)=0.0740"
## [1] "cor(responsive.fctr, use)=0.1660"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified law as highly correlated with use
## [1] "cor(improv, intern)=0.8319"
## [1] "cor(responsive.fctr, improv)=0.0741"
## [1] "cor(responsive.fctr, intern)=0.0337"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified intern as highly correlated with improv
## [1] "cor(buy, money)=0.8305"
## [1] "cor(responsive.fctr, buy)=0.1926"
## [1] "cor(responsive.fctr, money)=0.1923"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified money as highly correlated with buy
## [1] "cor(now, year)=0.8304"
## [1] "cor(responsive.fctr, now)=0.1798"
## [1] "cor(responsive.fctr, year)=0.1849"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified now as highly correlated with year
## [1] "cor(buy, tri)=0.8290"
## [1] "cor(responsive.fctr, buy)=0.1926"
## [1] "cor(responsive.fctr, tri)=0.1400"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified tri as highly correlated with buy
## [1] "cor(capit, improv)=0.8238"
## [1] "cor(responsive.fctr, capit)=0.0539"
## [1] "cor(responsive.fctr, improv)=0.0741"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified capit as highly correlated with improv
## [1] "cor(power, time)=0.8214"
## [1] "cor(responsive.fctr, power)=0.2827"
## [1] "cor(responsive.fctr, time)=0.1941"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified time as highly correlated with power
## [1] "cor(buy, year)=0.8197"
## [1] "cor(responsive.fctr, buy)=0.1926"
## [1] "cor(responsive.fctr, year)=0.1849"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified year as highly correlated with buy
## [1] "cor(buy, start)=0.8197"
## [1] "cor(responsive.fctr, buy)=0.1926"
## [1] "cor(responsive.fctr, start)=0.1502"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified start as highly correlated with buy
## [1] "cor(administr, buy)=0.8180"
## [1] "cor(responsive.fctr, administr)=0.1436"
## [1] "cor(responsive.fctr, buy)=0.1926"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified administr as highly correlated with buy
## [1] "cor(one, said)=0.8143"
## [1] "cor(responsive.fctr, one)=0.1944"
## [1] "cor(responsive.fctr, said)=0.2510"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified one as highly correlated with said
## [1] "cor(firm, improv)=0.8125"
## [1] "cor(responsive.fctr, firm)=0.0609"
## [1] "cor(responsive.fctr, improv)=0.0741"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified firm as highly correlated with improv
## [1] "cor(keep, power)=0.8125"
## [1] "cor(responsive.fctr, keep)=0.1639"
## [1] "cor(responsive.fctr, power)=0.2827"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified keep as highly correlated with power
## [1] "cor(demand, suppli)=0.8106"
## [1] "cor(responsive.fctr, demand)=0.3298"
## [1] "cor(responsive.fctr, suppli)=0.3118"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified suppli as highly correlated with demand
## [1] "cor(may, use)=0.8100"
## [1] "cor(responsive.fctr, may)=0.1668"
## [1] "cor(responsive.fctr, use)=0.1660"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified use as highly correlated with may
## [1] "cor(compani, said)=0.8090"
## [1] "cor(responsive.fctr, compani)=0.2016"
## [1] "cor(responsive.fctr, said)=0.2510"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified compani as highly correlated with said
## [1] "cor(agenc, feder)=0.8078"
## [1] "cor(responsive.fctr, agenc)=0.1663"
## [1] "cor(responsive.fctr, feder)=0.2514"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified agenc as highly correlated with feder
## [1] "cor(result, tariff)=0.8066"
## [1] "cor(responsive.fctr, result)=0.1967"
## [1] "cor(responsive.fctr, tariff)=0.1299"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified tariff as highly correlated with result
## [1] "cor(buy, recent)=0.8031"
## [1] "cor(responsive.fctr, buy)=0.1926"
## [1] "cor(responsive.fctr, recent)=0.1402"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified recent as highly correlated with buy
## [1] "cor(higher, take)=0.8031"
## [1] "cor(responsive.fctr, higher)=0.2522"
## [1] "cor(responsive.fctr, take)=0.2534"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified higher as highly correlated with take
## [1] "cor(anoth, buy)=0.8024"
## [1] "cor(responsive.fctr, anoth)=0.1816"
## [1] "cor(responsive.fctr, buy)=0.1926"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified anoth as highly correlated with buy
## [1] "cor(buy, that)=0.8021"
## [1] "cor(responsive.fctr, buy)=0.1926"
## [1] "cor(responsive.fctr, that)=0.1102"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified that as highly correlated with buy
## [1] "cor(buy, get)=0.8014"
## [1] "cor(responsive.fctr, buy)=0.1926"
## [1] "cor(responsive.fctr, get)=0.1825"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified get as highly correlated with buy
## [1] "cor(last, said)=0.8013"
## [1] "cor(responsive.fctr, last)=0.1875"
## [1] "cor(responsive.fctr, said)=0.2510"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified last as highly correlated with said
## [1] "cor(amount, power)=0.8012"
## [1] "cor(responsive.fctr, amount)=0.1709"
## [1] "cor(responsive.fctr, power)=0.2827"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified amount as highly correlated with power
## [1] "cor(competit, result)=0.7991"
## [1] "cor(responsive.fctr, competit)=0.2144"
## [1] "cor(responsive.fctr, result)=0.1967"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified result as highly correlated with competit
## [1] "cor(buy, power)=0.7972"
## [1] "cor(responsive.fctr, buy)=0.1926"
## [1] "cor(responsive.fctr, power)=0.2827"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified buy as highly correlated with power
## [1] "cor(power, take)=0.7957"
## [1] "cor(responsive.fctr, power)=0.2827"
## [1] "cor(responsive.fctr, take)=0.2534"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified take as highly correlated with power
## [1] "cor(realli, thing)=0.7949"
## [1] "cor(responsive.fctr, realli)=0.0758"
## [1] "cor(responsive.fctr, thing)=0.1226"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified realli as highly correlated with thing
## [1] "cor(power, util)=0.7943"
## [1] "cor(responsive.fctr, power)=0.2827"
## [1] "cor(responsive.fctr, util)=0.3013"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified power as highly correlated with util
## [1] "cor(copyright, said)=0.7915"
## [1] "cor(responsive.fctr, copyright)=0.2263"
## [1] "cor(responsive.fctr, said)=0.2510"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified copyright as highly correlated with said
## [1] "cor(public, southern)=0.7915"
## [1] "cor(responsive.fctr, public)=0.2326"
## [1] "cor(responsive.fctr, southern)=0.3031"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified public as highly correlated with southern
## [1] "cor(generat, X2000)=0.7895"
## [1] "cor(responsive.fctr, generat)=0.3176"
## [1] "cor(responsive.fctr, X2000)=0.2097"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified X2000 as highly correlated with generat
## [1] "cor(effici, improv)=0.7871"
## [1] "cor(responsive.fctr, effici)=0.1581"
## [1] "cor(responsive.fctr, improv)=0.0741"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified improv as highly correlated with effici
## [1] "cor(least, said)=0.7869"
## [1] "cor(responsive.fctr, least)=0.1529"
## [1] "cor(responsive.fctr, said)=0.2510"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified least as highly correlated with said
## [1] "cor(combin, seek)=0.7850"
## [1] "cor(responsive.fctr, combin)=0.1272"
## [1] "cor(responsive.fctr, seek)=0.1391"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified combin as highly correlated with seek
## [1] "cor(generat, price)=0.7820"
## [1] "cor(responsive.fctr, generat)=0.3176"
## [1] "cor(responsive.fctr, price)=0.3619"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified generat as highly correlated with price
## [1] "cor(particip, seek)=0.7813"
## [1] "cor(responsive.fctr, particip)=0.1396"
## [1] "cor(responsive.fctr, seek)=0.1391"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified seek as highly correlated with particip
## [1] "cor(know, let)=0.7811"
## [1] "cor(responsive.fctr, know)=0.0690"
## [1] "cor(responsive.fctr, let)=0.0505"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified let as highly correlated with know
## [1] "cor(good, said)=0.7801"
## [1] "cor(responsive.fctr, good)=0.1640"
## [1] "cor(responsive.fctr, said)=0.2510"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified good as highly correlated with said
## [1] "cor(competit, price)=0.7793"
## [1] "cor(responsive.fctr, competit)=0.2144"
## [1] "cor(responsive.fctr, price)=0.3619"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified competit as highly correlated with price
## [1] "cor(depart, privat)=0.7772"
## [1] "cor(responsive.fctr, depart)=0.0988"
## [1] "cor(responsive.fctr, privat)=0.1119"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified depart as highly correlated with privat
## [1] "cor(bid, submit)=0.7762"
## [1] "cor(responsive.fctr, bid)=0.1839"
## [1] "cor(responsive.fctr, submit)=0.0869"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified submit as highly correlated with bid
## [1] "cor(dasovich, jeff)=0.7757"
## [1] "cor(responsive.fctr, dasovich)=0.0922"
## [1] "cor(responsive.fctr, jeff)=0.1781"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified dasovich as highly correlated with jeff
## [1] "cor(limit, protect)=0.7747"
## [1] "cor(responsive.fctr, limit)=0.0875"
## [1] "cor(responsive.fctr, protect)=0.0648"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified protect as highly correlated with limit
## [1] "cor(governor, million)=0.7743"
## [1] "cor(responsive.fctr, governor)=0.1690"
## [1] "cor(responsive.fctr, million)=0.2247"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified governor as highly correlated with million
## [1] "cor(also, new)=0.7735"
## [1] "cor(responsive.fctr, also)=0.1949"
## [1] "cor(responsive.fctr, new)=0.1329"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified new as highly correlated with also
## [1] "cor(construct, technolog)=0.7717"
## [1] "cor(responsive.fctr, construct)=0.1723"
## [1] "cor(responsive.fctr, technolog)=0.2061"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified construct as highly correlated with technolog
## [1] "cor(can, make)=0.7707"
## [1] "cor(responsive.fctr, can)=0.2327"
## [1] "cor(responsive.fctr, make)=0.1612"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified make as highly correlated with can
## [1] "cor(bill, committe)=0.7699"
## [1] "cor(responsive.fctr, bill)=0.0964"
## [1] "cor(responsive.fctr, committe)=0.0265"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified committe as highly correlated with bill
## [1] "cor(may, system)=0.7696"
## [1] "cor(responsive.fctr, may)=0.1668"
## [1] "cor(responsive.fctr, system)=0.2556"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified may as highly correlated with system
## [1] "cor(grow, reduc)=0.7693"
## [1] "cor(responsive.fctr, grow)=0.1642"
## [1] "cor(responsive.fctr, reduc)=0.2141"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified grow as highly correlated with reduc
## [1] "cor(data, inform)=0.7676"
## [1] "cor(responsive.fctr, data)=0.0866"
## [1] "cor(responsive.fctr, inform)=0.1109"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified data as highly correlated with inform
## [1] "cor(ago, resourc)=0.7630"
## [1] "cor(responsive.fctr, ago)=0.1495"
## [1] "cor(responsive.fctr, resourc)=0.1781"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified ago as highly correlated with resourc
## [1] "cor(past, said)=0.7629"
## [1] "cor(responsive.fctr, past)=0.1969"
## [1] "cor(responsive.fctr, said)=0.2510"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified past as highly correlated with said
## [1] "cor(industri, said)=0.7628"
## [1] "cor(responsive.fctr, industri)=0.1924"
## [1] "cor(responsive.fctr, said)=0.2510"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified industri as highly correlated with said
## [1] "cor(lot, said)=0.7627"
## [1] "cor(responsive.fctr, lot)=0.1005"
## [1] "cor(responsive.fctr, said)=0.2510"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified lot as highly correlated with said
## [1] "cor(base, level)=0.7614"
## [1] "cor(responsive.fctr, base)=0.1833"
## [1] "cor(responsive.fctr, level)=0.2244"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified base as highly correlated with level
## [1] "cor(becom, said)=0.7611"
## [1] "cor(responsive.fctr, becom)=0.1243"
## [1] "cor(responsive.fctr, said)=0.2510"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified becom as highly correlated with said
## [1] "cor(eric, resourc)=0.7608"
## [1] "cor(responsive.fctr, eric)=0.0937"
## [1] "cor(responsive.fctr, resourc)=0.1781"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified eric as highly correlated with resourc
## [1] "cor(lower, order)=0.7604"
## [1] "cor(responsive.fctr, lower)=0.2326"
## [1] "cor(responsive.fctr, order)=0.2420"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified lower as highly correlated with order
## [1] "cor(also, need)=0.7600"
## [1] "cor(responsive.fctr, also)=0.1949"
## [1] "cor(responsive.fctr, need)=0.1552"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified need as highly correlated with also
## [1] "cor(materi, put)=0.7598"
## [1] "cor(responsive.fctr, materi)=0.1346"
## [1] "cor(responsive.fctr, put)=0.1202"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified put as highly correlated with materi
## [1] "cor(fall, said)=0.7578"
## [1] "cor(responsive.fctr, fall)=0.2082"
## [1] "cor(responsive.fctr, said)=0.2510"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified fall as highly correlated with said
## [1] "cor(among, price)=0.7575"
## [1] "cor(responsive.fctr, among)=0.2446"
## [1] "cor(responsive.fctr, price)=0.3619"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified among as highly correlated with price
## [1] "cor(allow, privat)=0.7535"
## [1] "cor(responsive.fctr, allow)=0.1799"
## [1] "cor(responsive.fctr, privat)=0.1119"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified privat as highly correlated with allow
## [1] "cor(independ, price)=0.7535"
## [1] "cor(responsive.fctr, independ)=0.3397"
## [1] "cor(responsive.fctr, price)=0.3619"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified independ as highly correlated with price
## [1] "cor(level, price)=0.7526"
## [1] "cor(responsive.fctr, level)=0.2244"
## [1] "cor(responsive.fctr, price)=0.3619"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified level as highly correlated with price
## [1] "cor(karen, resourc)=0.7525"
## [1] "cor(responsive.fctr, karen)=0.0867"
## [1] "cor(responsive.fctr, resourc)=0.1781"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified karen as highly correlated with resourc
## [1] "cor(lead, said)=0.7515"
## [1] "cor(responsive.fctr, lead)=0.1553"
## [1] "cor(responsive.fctr, said)=0.2510"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified lead as highly correlated with said
## [1] "cor(dollar, part)=0.7495"
## [1] "cor(responsive.fctr, dollar)=0.1761"
## [1] "cor(responsive.fctr, part)=0.2196"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified dollar as highly correlated with part
## [1] "cor(includ, inform)=0.7495"
## [1] "cor(responsive.fctr, includ)=0.1853"
## [1] "cor(responsive.fctr, inform)=0.1109"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified inform as highly correlated with includ
## [1] "cor(also, will)=0.7494"
## [1] "cor(responsive.fctr, also)=0.1949"
## [1] "cor(responsive.fctr, will)=0.2049"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified also as highly correlated with will
## [1] "cor(fuel, percent)=0.7484"
## [1] "cor(responsive.fctr, fuel)=0.2061"
## [1] "cor(responsive.fctr, percent)=0.1932"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified percent as highly correlated with fuel
## [1] "cor(intend, prohibit)=0.7478"
## [1] "cor(responsive.fctr, intend)=0.1061"
## [1] "cor(responsive.fctr, prohibit)=0.1486"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified intend as highly correlated with prohibit
## [1] "cor(avoid, thing)=0.7474"
## [1] "cor(responsive.fctr, avoid)=0.1595"
## [1] "cor(responsive.fctr, thing)=0.1226"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified thing as highly correlated with avoid
## [1] "cor(part, price)=0.7450"
## [1] "cor(responsive.fctr, part)=0.2196"
## [1] "cor(responsive.fctr, price)=0.3619"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified part as highly correlated with price
## [1] "cor(effici, system)=0.7441"
## [1] "cor(responsive.fctr, effici)=0.1581"
## [1] "cor(responsive.fctr, system)=0.2556"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified effici as highly correlated with system
## [1] "cor(consult, feder)=0.7436"
## [1] "cor(responsive.fctr, consult)=0.1400"
## [1] "cor(responsive.fctr, feder)=0.2514"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified consult as highly correlated with feder
## [1] "cor(resourc, X2001)=0.7431"
## [1] "cor(responsive.fctr, resourc)=0.1781"
## [1] "cor(responsive.fctr, X2001)=0.1332"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified X2001 as highly correlated with resourc
## [1] "cor(allow, reason)=0.7431"
## [1] "cor(responsive.fctr, allow)=0.1799"
## [1] "cor(responsive.fctr, reason)=0.1956"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified allow as highly correlated with reason
## [1] "cor(consid, rais)=0.7429"
## [1] "cor(responsive.fctr, consid)=0.1938"
## [1] "cor(responsive.fctr, rais)=0.2063"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified consid as highly correlated with rais
## [1] "cor(larg, said)=0.7413"
## [1] "cor(responsive.fctr, larg)=0.1740"
## [1] "cor(responsive.fctr, said)=0.2510"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified larg as highly correlated with said
## [1] "cor(cynthia, tom)=0.7406"
## [1] "cor(responsive.fctr, cynthia)=0.0716"
## [1] "cor(responsive.fctr, tom)=0.1259"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified cynthia as highly correlated with tom
## [1] "cor(continu, fact)=0.7400"
## [1] "cor(responsive.fctr, continu)=0.2508"
## [1] "cor(responsive.fctr, fact)=0.1666"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified fact as highly correlated with continu
## [1] "cor(feder, resourc)=0.7395"
## [1] "cor(responsive.fctr, feder)=0.2514"
## [1] "cor(responsive.fctr, resourc)=0.1781"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified resourc as highly correlated with feder
## [1] "cor(avoid, rais)=0.7379"
## [1] "cor(responsive.fctr, avoid)=0.1595"
## [1] "cor(responsive.fctr, rais)=0.2063"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified avoid as highly correlated with rais
## [1] "cor(bid, capac)=0.7377"
## [1] "cor(responsive.fctr, bid)=0.1839"
## [1] "cor(responsive.fctr, capac)=0.2682"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified bid as highly correlated with capac
## [1] "cor(cover, said)=0.7376"
## [1] "cor(responsive.fctr, cover)=0.1323"
## [1] "cor(responsive.fctr, said)=0.2510"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified cover as highly correlated with said
## [1] "cor(regulatori, system)=0.7367"
## [1] "cor(responsive.fctr, regulatori)=0.2132"
## [1] "cor(responsive.fctr, system)=0.2556"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified regulatori as highly correlated with system
## [1] "cor(system, trade)=0.7359"
## [1] "cor(responsive.fctr, system)=0.2556"
## [1] "cor(responsive.fctr, trade)=0.1002"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified trade as highly correlated with system
## [1] "cor(futur, late)=0.7348"
## [1] "cor(responsive.fctr, futur)=0.1116"
## [1] "cor(responsive.fctr, late)=0.0921"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified late as highly correlated with futur
## [1] "cor(million, nation)=0.7307"
## [1] "cor(responsive.fctr, million)=0.2247"
## [1] "cor(responsive.fctr, nation)=0.1942"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified nation as highly correlated with million
## [1] "cor(high, reduc)=0.7280"
## [1] "cor(responsive.fctr, high)=0.2878"
## [1] "cor(responsive.fctr, reduc)=0.2141"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified reduc as highly correlated with high
## [1] "cor(earli, said)=0.7278"
## [1] "cor(responsive.fctr, earli)=0.1861"
## [1] "cor(responsive.fctr, said)=0.2510"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified earli as highly correlated with said
## [1] "cor(said, still)=0.7278"
## [1] "cor(responsive.fctr, said)=0.2510"
## [1] "cor(responsive.fctr, still)=0.2106"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified still as highly correlated with said
## [1] "cor(continu, particip)=0.7247"
## [1] "cor(responsive.fctr, continu)=0.2508"
## [1] "cor(responsive.fctr, particip)=0.1396"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified particip as highly correlated with continu
## [1] "cor(edison, util)=0.7239"
## [1] "cor(responsive.fctr, edison)=0.1886"
## [1] "cor(responsive.fctr, util)=0.3013"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified edison as highly correlated with util
## [1] "cor(day, said)=0.7239"
## [1] "cor(responsive.fctr, day)=0.1901"
## [1] "cor(responsive.fctr, said)=0.2510"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified day as highly correlated with said
## [1] "cor(otherwis, period)=0.7237"
## [1] "cor(responsive.fctr, otherwis)=0.1126"
## [1] "cor(responsive.fctr, period)=0.1706"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified otherwis as highly correlated with period
## [1] "cor(better, said)=0.7236"
## [1] "cor(responsive.fctr, better)=0.1751"
## [1] "cor(responsive.fctr, said)=0.2510"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified better as highly correlated with said
## [1] "cor(littl, said)=0.7235"
## [1] "cor(responsive.fctr, littl)=0.1210"
## [1] "cor(responsive.fctr, said)=0.2510"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified littl as highly correlated with said
## [1] "cor(other, rais)=0.7231"
## [1] "cor(responsive.fctr, other)=0.2206"
## [1] "cor(responsive.fctr, rais)=0.2063"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified rais as highly correlated with other
## [1] "cor(feder, program)=0.7229"
## [1] "cor(responsive.fctr, feder)=0.2514"
## [1] "cor(responsive.fctr, program)=0.1503"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified program as highly correlated with feder
## [1] "cor(gas, natur)=0.7209"
## [1] "cor(responsive.fctr, gas)=0.2688"
## [1] "cor(responsive.fctr, natur)=0.2831"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified gas as highly correlated with natur
## [1] "cor(although, futur)=0.7209"
## [1] "cor(responsive.fctr, although)=0.1485"
## [1] "cor(responsive.fctr, futur)=0.1116"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified futur as highly correlated with although
## [1] "cor(month, spot)=0.7196"
## [1] "cor(responsive.fctr, month)=0.1579"
## [1] "cor(responsive.fctr, spot)=0.1859"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified month as highly correlated with spot
## [1] "cor(demand, summer)=0.7192"
## [1] "cor(responsive.fctr, demand)=0.3298"
## [1] "cor(responsive.fctr, summer)=0.3053"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified summer as highly correlated with demand
## [1] "cor(million, news)=0.7186"
## [1] "cor(responsive.fctr, million)=0.2247"
## [1] "cor(responsive.fctr, news)=0.2123"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified news as highly correlated with million
## [1] "cor(continu, includ)=0.7183"
## [1] "cor(responsive.fctr, continu)=0.2508"
## [1] "cor(responsive.fctr, includ)=0.1853"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified includ as highly correlated with continu
## [1] "cor(ferc, order)=0.7171"
## [1] "cor(responsive.fctr, ferc)=0.3039"
## [1] "cor(responsive.fctr, order)=0.2420"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified order as highly correlated with ferc
## [1] "cor(complet, regul)=0.7154"
## [1] "cor(responsive.fctr, complet)=0.1166"
## [1] "cor(responsive.fctr, regul)=0.1292"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified complet as highly correlated with regul
## [1] "cor(can, high)=0.7153"
## [1] "cor(responsive.fctr, can)=0.2327"
## [1] "cor(responsive.fctr, high)=0.2878"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified can as highly correlated with high
## [1] "cor(million, util)=0.7149"
## [1] "cor(responsive.fctr, million)=0.2247"
## [1] "cor(responsive.fctr, util)=0.3013"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified million as highly correlated with util
## [1] "cor(charg, period)=0.7146"
## [1] "cor(responsive.fctr, charg)=0.1772"
## [1] "cor(responsive.fctr, period)=0.1706"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified period as highly correlated with charg
## [1] "cor(oper, system)=0.7142"
## [1] "cor(responsive.fctr, oper)=0.2864"
## [1] "cor(responsive.fctr, system)=0.2556"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified system as highly correlated with oper
## [1] "cor(price, southern)=0.7135"
## [1] "cor(responsive.fctr, price)=0.3619"
## [1] "cor(responsive.fctr, southern)=0.3031"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified southern as highly correlated with price
## [1] "cor(smith, X1400)=0.7126"
## [1] "cor(responsive.fctr, smith)=-0.0356"
## [1] "cor(responsive.fctr, X1400)=-0.0616"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified smith as highly correlated with X1400
## [1] "cor(general, limit)=0.7121"
## [1] "cor(responsive.fctr, general)=0.0961"
## [1] "cor(responsive.fctr, limit)=0.0875"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified limit as highly correlated with general
## [1] "cor(contract, long)=0.7120"
## [1] "cor(responsive.fctr, contract)=0.1747"
## [1] "cor(responsive.fctr, long)=0.1321"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified long as highly correlated with contract
## [1] "cor(high, said)=0.7117"
## [1] "cor(responsive.fctr, high)=0.2878"
## [1] "cor(responsive.fctr, said)=0.2510"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified said as highly correlated with high
## [1] "cor(charg, member)=0.7108"
## [1] "cor(responsive.fctr, charg)=0.1772"
## [1] "cor(responsive.fctr, member)=0.1260"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified member as highly correlated with charg
## [1] "cor(cost, sinc)=0.7102"
## [1] "cor(responsive.fctr, cost)=0.2299"
## [1] "cor(responsive.fctr, sinc)=0.1809"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified sinc as highly correlated with cost
## [1] "cor(feder, regul)=0.7090"
## [1] "cor(responsive.fctr, feder)=0.2514"
## [1] "cor(responsive.fctr, regul)=0.1292"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified regul as highly correlated with feder
## [1] "cor(court, particular)=0.7052"
## [1] "cor(responsive.fctr, court)=0.1217"
## [1] "cor(responsive.fctr, particular)=0.1247"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified court as highly correlated with particular
## [1] "cor(mean, standard)=0.7046"
## [1] "cor(responsive.fctr, mean)=0.0676"
## [1] "cor(responsive.fctr, standard)=0.0716"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified mean as highly correlated with standard
## [1] "cor(europ, global)=0.7043"
## [1] "cor(responsive.fctr, europ)=-0.0313"
## [1] "cor(responsive.fctr, global)=-0.0258"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified global as highly correlated with europ
## [1] "cor(face, feder)=0.7041"
## [1] "cor(responsive.fctr, face)=0.2023"
## [1] "cor(responsive.fctr, feder)=0.2514"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified face as highly correlated with feder
## [1] "cor(materi, turn)=0.7037"
## [1] "cor(responsive.fctr, materi)=0.1346"
## [1] "cor(responsive.fctr, turn)=0.1729"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified materi as highly correlated with turn
## [1] "cor(rather, south)=0.7010"
## [1] "cor(responsive.fctr, rather)=0.1675"
## [1] "cor(responsive.fctr, south)=0.0748"## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?
## geom_smooth: Only one unique x value each group.Maybe you want aes(group = 1)?## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified south as highly correlated with rather
## id cor.y exclude.as.feat
## responsive responsive 1.0000000000 1
## price price 0.3618825045 0
## independ independ 0.3396874146 0
## demand demand 0.3297669856 0
## generat generat 0.3176296544 0
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## ferc ferc 0.3038831190 0
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## electr electr 0.2274094320 0
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## steffesnaenronenron steffesnaenronenron 0.2117758013 0
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## technolog FALSE
## avail FALSE
## will FALSE
## today FALSE
## full FALSE
## unit FALSE
## face FALSE
## pass FALSE
## compani FALSE
## requir FALSE
## cut FALSE
## point FALSE
## step FALSE
## even FALSE
## andor FALSE
## earlier FALSE
## design FALSE
## past FALSE
## result FALSE
## pacif FALSE
## sell FALSE
## mani FALSE
## reason FALSE
## also FALSE
## reserv FALSE
## one FALSE
## nation FALSE
## time FALSE
## consid FALSE
## billion FALSE
## percent FALSE
## deregul FALSE
## buy FALSE
## industri FALSE
## money FALSE
## key FALSE
## presid FALSE
## day FALSE
## set FALSE
## remain FALSE
## issu FALSE
## success FALSE
## plan FALSE
## edison FALSE
## call FALSE
## accord FALSE
## last FALSE
## sever FALSE
## own FALSE
## earli FALSE
## spot FALSE
## includ FALSE
## year FALSE
## appear FALSE
## bid FALSE
## base FALSE
## balanc FALSE
## get FALSE
## staff FALSE
## anoth FALSE
## deliveri FALSE
## sinc FALSE
## got FALSE
## control FALSE
## allow FALSE
## now FALSE
## resourc FALSE
## jeff FALSE
## small FALSE
## articl FALSE
## charg FALSE
## per FALSE
## washington FALSE
## dollar FALSE
## better FALSE
## seller FALSE
## contract FALSE
## benefit FALSE
## board FALSE
## larg FALSE
## wednesday FALSE
## help FALSE
## near FALSE
## sold FALSE
## term FALSE
## turn FALSE
## construct FALSE
## five FALSE
## much FALSE
## like FALSE
## around FALSE
## amount FALSE
## direct FALSE
## period FALSE
## top FALSE
## structur FALSE
## offici FALSE
## governor FALSE
## signific FALSE
## meet FALSE
## rather FALSE
## end FALSE
## friday FALSE
## report FALSE
## may FALSE
## fact FALSE
## dont FALSE
## agenc FALSE
## offer FALSE
## use FALSE
## follow FALSE
## file FALSE
## taken FALSE
## purpos FALSE
## opinion FALSE
## talk FALSE
## certain FALSE
## grow FALSE
## good FALSE
## keep FALSE
## altern FALSE
## busi FALSE
## dasovichnaenron FALSE
## exist FALSE
## come FALSE
## make FALSE
## real FALSE
## drive FALSE
## avoid FALSE
## pge FALSE
## monday FALSE
## effort FALSE
## effici FALSE
## month FALSE
## back FALSE
## repres FALSE
## measur FALSE
## davi FALSE
## determin FALSE
## exchang FALSE
## develop FALSE
## lead FALSE
## need FALSE
## januari FALSE
## payment FALSE
## caus FALSE
## immedi FALSE
## least FALSE
## run FALSE
## explain FALSE
## messag FALSE
## havent FALSE
## next. FALSE
## tuesday FALSE
## occur FALSE
## san FALSE
## receiv FALSE
## program FALSE
## upon FALSE
## start FALSE
## refer FALSE
## ago FALSE
## prohibit FALSE
## approxim FALSE
## although FALSE
## differ FALSE
## releas FALSE
## legisl FALSE
## analysi FALSE
## just FALSE
## later FALSE
## open FALSE
## chairman FALSE
## region FALSE
## move FALSE
## clear FALSE
## administr FALSE
## forward FALSE
## post FALSE
## director FALSE
## decemb FALSE
## produc FALSE
## believ FALSE
## estim FALSE
## alreadi FALSE
## fix FALSE
## found FALSE
## recent FALSE
## tri FALSE
## consult FALSE
## facil FALSE
## particip FALSE
## trader FALSE
## seek FALSE
## see FALSE
## novemb FALSE
## crisi FALSE
## cpuc FALSE
## richard FALSE
## far FALSE
## entir FALSE
## investig FALSE
## polici FALSE
## retail FALSE
## indic FALSE
## free FALSE
## execut FALSE
## respons FALSE
## materi FALSE
## modifi FALSE
## X100 FALSE
## citi FALSE
## X2001 FALSE
## steven FALSE
## new FALSE
## third FALSE
## cover FALSE
## oblig FALSE
## long FALSE
## portion FALSE
## august FALSE
## suggest FALSE
## basi FALSE
## idea FALSE
## return FALSE
## possibl FALSE
## tariff FALSE
## procedur FALSE
## might FALSE
## person FALSE
## regul FALSE
## readi FALSE
## condit FALSE
## agre FALSE
## though FALSE
## due FALSE
## four FALSE
## combin FALSE
## X1999 FALSE
## big FALSE
## someth FALSE
## comput FALSE
## look FALSE
## averag FALSE
## replac FALSE
## everi FALSE
## member FALSE
## press FALSE
## tom FALSE
## record FALSE
## applic FALSE
## focus FALSE
## way FALSE
## hold FALSE
## particular FALSE
## short FALSE
## decis FALSE
## becom FALSE
## jame FALSE
## connect FALSE
## corp FALSE
## posit FALSE
## thing FALSE
## coupl FALSE
## howev FALSE
## court FALSE
## manag FALSE
## potenti FALSE
## littl FALSE
## juli FALSE
## thursday FALSE
## made FALSE
## put FALSE
## detail FALSE
## begin FALSE
## answer FALSE
## site FALSE
## well FALSE
## must FALSE
## total FALSE
## excess FALSE
## effect FALSE
## enter FALSE
## involv FALSE
## complet FALSE
## chanc FALSE
## togeth FALSE
## negoti FALSE
## therefor FALSE
## advanc FALSE
## notic FALSE
## ensur FALSE
## brian FALSE
## except FALSE
## home FALSE
## morn FALSE
## give FALSE
## act FALSE
## storag FALSE
## accept FALSE
## coordin FALSE
## unless FALSE
## confer FALSE
## otherwis FALSE
## privat FALSE
## futur FALSE
## publish FALSE
## share FALSE
## fail FALSE
## web FALSE
## inform FALSE
## given FALSE
## arrang FALSE
## that FALSE
## statement FALSE
## project FALSE
## soon FALSE
## current FALSE
## second FALSE
## question FALSE
## first FALSE
## week FALSE
## william FALSE
## ask FALSE
## commod FALSE
## say FALSE
## hope FALSE
## schedul FALSE
## intend FALSE
## jim FALSE
## reach FALSE
## less FALSE
## case FALSE
## outsid FALSE
## daili FALSE
## section FALSE
## instead FALSE
## name FALSE
## place FALSE
## rule FALSE
## guarante FALSE
## address FALSE
## govern FALSE
## associ FALSE
## practic FALSE
## subject FALSE
## lot FALSE
## access FALSE
## trade FALSE
## locat FALSE
## susan FALSE
## model FALSE
## invest FALSE
## main FALSE
## build FALSE
## depart FALSE
## financ FALSE
## note FALSE
## left FALSE
## identifi FALSE
## internet FALSE
## reflect FALSE
## actual FALSE
## express FALSE
## bill FALSE
## general FALSE
## support FALSE
## import FALSE
## qualiti FALSE
## confidenti FALSE
## view FALSE
## show FALSE
## comment FALSE
## peopl FALSE
## eric FALSE
## correct FALSE
## work FALSE
## similar FALSE
## assum FALSE
## fund FALSE
## dasovich FALSE
## late FALSE
## sign FALSE
## seem FALSE
## seen FALSE
## cash FALSE
## option FALSE
## tell FALSE
## add FALSE
## word FALSE
## commerci FALSE
## flow FALSE
## sale FALSE
## limit FALSE
## attorney FALSE
## previous FALSE
## concern FALSE
## submit FALSE
## karen FALSE
## approach FALSE
## data FALSE
## necessari FALSE
## decid FALSE
## various FALSE
## recommend FALSE
## abl FALSE
## along FALSE
## water FALSE
## physic FALSE
## claim FALSE
## final FALSE
## error FALSE
## number FALSE
## event FALSE
## think FALSE
## paul FALSE
## alan FALSE
## april FALSE
## parti FALSE
## exampl FALSE
## els FALSE
## group FALSE
## anyon FALSE
## dissemin FALSE
## distribut FALSE
## onlin FALSE
## realli FALSE
## south FALSE
## delet FALSE
## improv FALSE
## sue FALSE
## law FALSE
## individu FALSE
## oil FALSE
## prepar FALSE
## hear FALSE
## leav FALSE
## advis FALSE
## head FALSE
## standard FALSE
## cynthia FALSE
## joe FALSE
## notifi FALSE
## speak FALSE
## thought FALSE
## jan FALSE
## tim FALSE
## know FALSE
## awar FALSE
## strong FALSE
## page FALSE
## mean FALSE
## sender FALSE
## activ FALSE
## organ FALSE
## east FALSE
## present FALSE
## protect FALSE
## corpor FALSE
## west FALSE
## kevin FALSE
## enron FALSE
## initi FALSE
## singl FALSE
## area FALSE
## relat FALSE
## kind FALSE
## everyon FALSE
## firm FALSE
## electron FALSE
## extend FALSE
## assist FALSE
## affect FALSE
## octob FALSE
## origin FALSE
## specif FALSE
## attent FALSE
## septemb FALSE
## deliv FALSE
## chang FALSE
## york FALSE
## rob FALSE
## hard FALSE
## north FALSE
## valu FALSE
## termin FALSE
## capit FALSE
## offic FALSE
## either FALSE
## calcul FALSE
## yet FALSE
## ill FALSE
## consider FALSE
## side FALSE
## approv FALSE
## request FALSE
## shall FALSE
## asset FALSE
## product FALSE
## feel FALSE
## let FALSE
## carol FALSE
## someon FALSE
## march FALSE
## probabl FALSE
## sent FALSE
## commit FALSE
## appropri FALSE
## matter FALSE
## depend FALSE
## research FALSE
## chris FALSE
## keannaenronenron FALSE
## quick FALSE
## contain FALSE
## hand FALSE
## gari FALSE
## respect FALSE
## steve FALSE
## guy FALSE
## date FALSE
## deal FALSE
## tomorrow FALSE
## repli FALSE
## latest FALSE
## interest FALSE
## extens FALSE
## scott FALSE
## financi FALSE
## liquid FALSE
## texa FALSE
## local FALSE
## intern FALSE
## entiti FALSE
## vinc FALSE
## anyth FALSE
## team FALSE
## read FALSE
## great FALSE
## via FALSE
## risk FALSE
## impact FALSE
## proceed FALSE
## desk FALSE
## ive FALSE
## done FALSE
## harri FALSE
## llc FALSE
## afternoon FALSE
## mari FALSE
## michael FALSE
## cours FALSE
## best FALSE
## perform FALSE
## link FALSE
## delay FALSE
## account FALSE
## committe FALSE
## opportun FALSE
## privileg FALSE
## process FALSE
## find FALSE
## sure FALSE
## mail FALSE
## greg FALSE
## send FALSE
## legal FALSE
## recipi TRUE
## london TRUE
## transact TRUE
## confirm TRUE
## mention TRUE
## summari TRUE
## convers TRUE
## prior TRUE
## visit TRUE
## respond TRUE
## bit TRUE
## yesterday TRUE
## websit TRUE
## format TRUE
## review TRUE
## paper TRUE
## georg TRUE
## peter TRUE
## handl TRUE
## kelli TRUE
## item TRUE
## employe TRUE
## net TRUE
## provis TRUE
## februari TRUE
## suit TRUE
## frank TRUE
## robert TRUE
## attend TRUE
## strategi TRUE
## dave TRUE
## email.1 TRUE
## street TRUE
## join TRUE
## dan TRUE
## list TRUE
## ken TRUE
## met TRUE
## appreci TRUE
## draft TRUE
## contact TRUE
## status TRUE
## bob TRUE
## version TRUE
## type TRUE
## appli TRUE
## sorri TRUE
## mark TRUE
## credit TRUE
## understand TRUE
## kaminskihouect TRUE
## etc TRUE
## rick TRUE
## brief TRUE
## communic TRUE
## document TRUE
## bank TRUE
## sheet TRUE
## phone TRUE
## gerald TRUE
## ena TRUE
## copi TRUE
## fyi TRUE
## counsel TRUE
## john TRUE
## updat TRUE
## jeffrey TRUE
## roger TRUE
## lesli TRUE
## lisa TRUE
## agreement TRUE
## volum TRUE
## letter TRUE
## elizabeth TRUE
## .rnorm FALSE
## settlement FALSE
## discuss FALSE
## houston FALSE
## global FALSE
## info FALSE
## transfer FALSE
## pleas FALSE
## america FALSE
## europ FALSE
## smith FALSE
## stephani FALSE
## memo FALSE
## dear FALSE
## regard FALSE
## fax FALSE
## form FALSE
## counterparti FALSE
## languag FALSE
## mike FALSE
## master FALSE
## X77002 FALSE
## david FALSE
## check FALSE
## print FALSE
## amend FALSE
## eol FALSE
## kate FALSE
## attach FALSE
## book FALSE
## X1400 FALSE
## X713 FALSE
## revis FALSE
## thank FALSE
## joneshouectect FALSE
## andrew FALSE
## tana FALSE
## taylorhouectect FALSE
## sara FALSEglb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="fit.models",
chunk_step_major=max(glb_script_df$chunk_step_major)+1,
chunk_step_minor=0,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))## chunk_label chunk_step_major chunk_step_minor
## elapsed7 remove_correlated_features 4 1
## elapsed8 fit.models 5 0
## elapsed
## elapsed7 24.394
## elapsed8 474.244Step 5: fit models
if (glb_is_classification && glb_is_binomial &&
(length(unique(glb_trnent_df[, glb_rsp_var])) < 2))
stop("glb_trnent_df$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glb_trnent_df[, glb_rsp_var]), collapse=", "))
max_cor_y_x_var <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low))[1, "id"]
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_var != glb_Baseline_mdl_var) &
(glb_feats_df[max_cor_y_x_var, "cor.y.abs"] >
glb_feats_df[glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_var, " has a lower correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Baseline
if (!is.null(glb_Baseline_mdl_var))
ret_lst <- myfit_mdl_fn(model_id="Baseline", model_method="mybaseln_classfr",
indep_vars_vctr=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df)
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
ret_lst <- myfit_mdl(model_id="MFO",
model_method=ifelse(glb_is_regression, "lm", "myMFO_classfr"),
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df)## [1] "fitting model: MFO.myMFO_classfr"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] N Y
## Levels: N Y
## [1] "unique.prob:"
## y
## N Y
## 0.8377926 0.1622074
## [1] "MFO.val:"
## [1] "N"
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 -none- numeric
## MFO.val 1 -none- character
## x.names 1 -none- character
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character## Loading required package: ROCR
## Loading required package: gplots
##
## Attaching package: 'gplots'
##
## The following object is masked from 'package:stats':
##
## lowess## [1] "in MFO.Classifier$predict"
## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.8377926 0.1622074
## 2 0.8377926 0.1622074
## 3 0.8377926 0.1622074
## 4 0.8377926 0.1622074
## 5 0.8377926 0.1622074
## 6 0.8377926 0.1622074
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## responsive.fctr responsive.fctr.predict.MFO.myMFO_classfr.N
## 1 N 501
## 2 Y 97
## Reference
## Prediction N Y
## N 501 97
## Y 0 0
## responsive.fctr responsive.fctr.predict.MFO.myMFO_classfr.N
## 1 N 501
## 2 Y 97
## responsive.fctr.predict.MFO.myMFO_classfr.Y
## 1 0
## 2 0
## Prediction
## Reference N Y
## N 501 0
## Y 97 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.377926e-01 0.000000e+00 8.057562e-01 8.664468e-01 8.377926e-01
## AccuracyPValue McnemarPValue
## 5.270745e-01 1.893964e-22
## [1] "in MFO.Classifier$predict"
## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.8377926 0.1622074
## 2 0.8377926 0.1622074
## 3 0.8377926 0.1622074
## 4 0.8377926 0.1622074
## 5 0.8377926 0.1622074
## 6 0.8377926 0.1622074
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## responsive.fctr responsive.fctr.predict.MFO.myMFO_classfr.N
## 1 N 215
## 2 Y 42
## Reference
## Prediction N Y
## N 215 42
## Y 0 0
## responsive.fctr responsive.fctr.predict.MFO.myMFO_classfr.N
## 1 N 215
## 2 Y 42
## responsive.fctr.predict.MFO.myMFO_classfr.Y
## 1 0
## 2 0
## Prediction
## Reference N Y
## N 215 0
## Y 42 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.365759e-01 0.000000e+00 7.855868e-01 8.796081e-01 8.365759e-01
## AccuracyPValue McnemarPValue
## 5.410773e-01 2.508861e-10
## model_id model_method feats max.nTuningRuns
## 1 MFO.myMFO_classfr myMFO_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.476 0.002 0.5
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0 0.8377926
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8057562 0.8664468 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.8365759
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7855868 0.8796081 0if (glb_is_classification)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
ret_lst <- myfit_mdl(model_id="Random", model_method="myrandom_classfr",
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df)## [1] "fitting model: Random.myrandom_classfr"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 table numeric
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] "in Random.Classifier$prob"
## Reference
## Prediction N Y
## N 0 0
## Y 501 97
## responsive.fctr responsive.fctr.predict.Random.myrandom_classfr.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Random.myrandom_classfr.Y
## 1 501
## 2 97
## Reference
## Prediction N Y
## N 0 0
## Y 501 97
## responsive.fctr responsive.fctr.predict.Random.myrandom_classfr.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Random.myrandom_classfr.Y
## 1 501
## 2 97
## Reference
## Prediction N Y
## N 434 86
## Y 67 11
## responsive.fctr responsive.fctr.predict.Random.myrandom_classfr.N
## 1 N 434
## 2 Y 86
## responsive.fctr.predict.Random.myrandom_classfr.Y
## 1 67
## 2 11
## Reference
## Prediction N Y
## N 434 86
## Y 67 11
## responsive.fctr responsive.fctr.predict.Random.myrandom_classfr.N
## 1 N 434
## 2 Y 86
## responsive.fctr.predict.Random.myrandom_classfr.Y
## 1 67
## 2 11
## Reference
## Prediction N Y
## N 434 86
## Y 67 11
## responsive.fctr responsive.fctr.predict.Random.myrandom_classfr.N
## 1 N 434
## 2 Y 86
## responsive.fctr.predict.Random.myrandom_classfr.Y
## 1 67
## 2 11
## Reference
## Prediction N Y
## N 434 86
## Y 67 11
## responsive.fctr responsive.fctr.predict.Random.myrandom_classfr.N
## 1 N 434
## 2 Y 86
## responsive.fctr.predict.Random.myrandom_classfr.Y
## 1 67
## 2 11
## Reference
## Prediction N Y
## N 434 86
## Y 67 11
## responsive.fctr responsive.fctr.predict.Random.myrandom_classfr.N
## 1 N 434
## 2 Y 86
## responsive.fctr.predict.Random.myrandom_classfr.Y
## 1 67
## 2 11
## Reference
## Prediction N Y
## N 434 86
## Y 67 11
## responsive.fctr responsive.fctr.predict.Random.myrandom_classfr.N
## 1 N 434
## 2 Y 86
## responsive.fctr.predict.Random.myrandom_classfr.Y
## 1 67
## 2 11
## Reference
## Prediction N Y
## N 434 86
## Y 67 11
## responsive.fctr responsive.fctr.predict.Random.myrandom_classfr.N
## 1 N 434
## 2 Y 86
## responsive.fctr.predict.Random.myrandom_classfr.Y
## 1 67
## 2 11
## Reference
## Prediction N Y
## N 501 97
## Y 0 0
## responsive.fctr responsive.fctr.predict.Random.myrandom_classfr.N
## 1 N 501
## 2 Y 97
## responsive.fctr.predict.Random.myrandom_classfr.Y
## 1 0
## 2 0
## Reference
## Prediction N Y
## N 501 97
## Y 0 0
## responsive.fctr responsive.fctr.predict.Random.myrandom_classfr.N
## 1 N 501
## 2 Y 97
## responsive.fctr.predict.Random.myrandom_classfr.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2791367
## 2 0.1 0.2791367
## 3 0.2 0.1257143
## 4 0.3 0.1257143
## 5 0.4 0.1257143
## 6 0.5 0.1257143
## 7 0.6 0.1257143
## 8 0.7 0.1257143
## 9 0.8 0.1257143
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.1000 to maximize f.score.fit"
## responsive.fctr responsive.fctr.predict.Random.myrandom_classfr.Y
## 1 N 501
## 2 Y 97
## Reference
## Prediction N Y
## N 0 0
## Y 501 97
## responsive.fctr responsive.fctr.predict.Random.myrandom_classfr.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Random.myrandom_classfr.Y
## 1 501
## 2 97
## Prediction
## Reference N Y
## N 0 501
## Y 0 97
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 1.622074e-01 0.000000e+00 1.335532e-01 1.942438e-01 8.377926e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 1.567171e-110
## [1] "in Random.Classifier$prob"
## Reference
## Prediction N Y
## N 0 0
## Y 215 42
## responsive.fctr responsive.fctr.predict.Random.myrandom_classfr.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Random.myrandom_classfr.Y
## 1 215
## 2 42
## Reference
## Prediction N Y
## N 0 0
## Y 215 42
## responsive.fctr responsive.fctr.predict.Random.myrandom_classfr.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Random.myrandom_classfr.Y
## 1 215
## 2 42
## Reference
## Prediction N Y
## N 181 36
## Y 34 6
## responsive.fctr responsive.fctr.predict.Random.myrandom_classfr.N
## 1 N 181
## 2 Y 36
## responsive.fctr.predict.Random.myrandom_classfr.Y
## 1 34
## 2 6
## Reference
## Prediction N Y
## N 181 36
## Y 34 6
## responsive.fctr responsive.fctr.predict.Random.myrandom_classfr.N
## 1 N 181
## 2 Y 36
## responsive.fctr.predict.Random.myrandom_classfr.Y
## 1 34
## 2 6
## Reference
## Prediction N Y
## N 181 36
## Y 34 6
## responsive.fctr responsive.fctr.predict.Random.myrandom_classfr.N
## 1 N 181
## 2 Y 36
## responsive.fctr.predict.Random.myrandom_classfr.Y
## 1 34
## 2 6
## Reference
## Prediction N Y
## N 181 36
## Y 34 6
## responsive.fctr responsive.fctr.predict.Random.myrandom_classfr.N
## 1 N 181
## 2 Y 36
## responsive.fctr.predict.Random.myrandom_classfr.Y
## 1 34
## 2 6
## Reference
## Prediction N Y
## N 181 36
## Y 34 6
## responsive.fctr responsive.fctr.predict.Random.myrandom_classfr.N
## 1 N 181
## 2 Y 36
## responsive.fctr.predict.Random.myrandom_classfr.Y
## 1 34
## 2 6
## Reference
## Prediction N Y
## N 181 36
## Y 34 6
## responsive.fctr responsive.fctr.predict.Random.myrandom_classfr.N
## 1 N 181
## 2 Y 36
## responsive.fctr.predict.Random.myrandom_classfr.Y
## 1 34
## 2 6
## Reference
## Prediction N Y
## N 181 36
## Y 34 6
## responsive.fctr responsive.fctr.predict.Random.myrandom_classfr.N
## 1 N 181
## 2 Y 36
## responsive.fctr.predict.Random.myrandom_classfr.Y
## 1 34
## 2 6
## Reference
## Prediction N Y
## N 215 42
## Y 0 0
## responsive.fctr responsive.fctr.predict.Random.myrandom_classfr.N
## 1 N 215
## 2 Y 42
## responsive.fctr.predict.Random.myrandom_classfr.Y
## 1 0
## 2 0
## Reference
## Prediction N Y
## N 215 42
## Y 0 0
## responsive.fctr responsive.fctr.predict.Random.myrandom_classfr.N
## 1 N 215
## 2 Y 42
## responsive.fctr.predict.Random.myrandom_classfr.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2809365
## 2 0.1 0.2809365
## 3 0.2 0.1463415
## 4 0.3 0.1463415
## 5 0.4 0.1463415
## 6 0.5 0.1463415
## 7 0.6 0.1463415
## 8 0.7 0.1463415
## 9 0.8 0.1463415
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.1000 to maximize f.score.OOB"
## responsive.fctr responsive.fctr.predict.Random.myrandom_classfr.Y
## 1 N 215
## 2 Y 42
## Reference
## Prediction N Y
## N 0 0
## Y 215 42
## responsive.fctr responsive.fctr.predict.Random.myrandom_classfr.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Random.myrandom_classfr.Y
## 1 215
## 2 42
## Prediction
## Reference N Y
## N 0 215
## Y 0 42
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 1.634241e-01 0.000000e+00 1.203919e-01 2.144132e-01 8.365759e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 3.036391e-48
## model_id model_method feats max.nTuningRuns
## 1 Random.myrandom_classfr myrandom_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.297 0.002 0.4898348
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.1 0.2791367 0.1622074
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.1335532 0.1942438 0 0.4923588
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.1 0.2809365 0.1634241
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.1203919 0.2144132 0# Any models that have tuning parameters has "better" results with cross-validation
# (except rf) & "different" results for different outcome metrics
# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df)## [1] "fitting model: Max.cor.Y.cv.0.rpart"
## [1] " indep_vars: price"## Loading required package: rpart## Fitting cp = 0.196 on full training set## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 598
##
## CP nsplit rel error
## 1 0.1958763 0 1
##
## Node number 1: 598 observations
## predicted class=N expected loss=0.1622074 P(node) =1
## class counts: 501 97
## probabilities: 0.838 0.162
##
## n= 598
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 598 97 N (0.8377926 0.1622074) *
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## responsive.fctr responsive.fctr.predict.Max.cor.Y.cv.0.rpart.N
## 1 N 501
## 2 Y 97
## Reference
## Prediction N Y
## N 501 97
## Y 0 0
## responsive.fctr responsive.fctr.predict.Max.cor.Y.cv.0.rpart.N
## 1 N 501
## 2 Y 97
## responsive.fctr.predict.Max.cor.Y.cv.0.rpart.Y
## 1 0
## 2 0
## Prediction
## Reference N Y
## N 501 0
## Y 97 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.377926e-01 0.000000e+00 8.057562e-01 8.664468e-01 8.377926e-01
## AccuracyPValue McnemarPValue
## 5.270745e-01 1.893964e-22
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## responsive.fctr responsive.fctr.predict.Max.cor.Y.cv.0.rpart.N
## 1 N 215
## 2 Y 42
## Reference
## Prediction N Y
## N 215 42
## Y 0 0
## responsive.fctr responsive.fctr.predict.Max.cor.Y.cv.0.rpart.N
## 1 N 215
## 2 Y 42
## responsive.fctr.predict.Max.cor.Y.cv.0.rpart.Y
## 1 0
## 2 0
## Prediction
## Reference N Y
## N 215 0
## Y 42 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.365759e-01 0.000000e+00 7.855868e-01 8.796081e-01 8.365759e-01
## AccuracyPValue McnemarPValue
## 5.410773e-01 2.508861e-10
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.cv.0.rpart rpart price 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.642 0.014 0.5
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0 0.8377926
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8057562 0.8664468 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.8365759
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7855868 0.8796081 0ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0.cp.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df,
n_cv_folds=0,
tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))## [1] "fitting model: Max.cor.Y.cv.0.cp.0.rpart"
## [1] " indep_vars: price"
## Fitting cp = 0 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 598
##
## CP nsplit rel error
## 1 0.1958763 0 1.0000000
## 2 0.0257732 1 0.8041237
## 3 0.0000000 3 0.7525773
##
## Variable importance
## price
## 100
##
## Node number 1: 598 observations, complexity param=0.1958763
## predicted class=N expected loss=0.1622074 P(node) =1
## class counts: 501 97
## probabilities: 0.838 0.162
## left son=2 (519 obs) right son=3 (79 obs)
## Primary splits:
## price < 1.5 to the left, improve=38.1952, (0 missing)
##
## Node number 2: 519 observations
## predicted class=N expected loss=0.09248555 P(node) =0.867893
## class counts: 471 48
## probabilities: 0.908 0.092
##
## Node number 3: 79 observations, complexity param=0.0257732
## predicted class=Y expected loss=0.3797468 P(node) =0.132107
## class counts: 30 49
## probabilities: 0.380 0.620
## left son=6 (49 obs) right son=7 (30 obs)
## Primary splits:
## price < 5.5 to the left, improve=3.125394, (0 missing)
##
## Node number 6: 49 observations, complexity param=0.0257732
## predicted class=Y expected loss=0.4897959 P(node) =0.0819398
## class counts: 24 25
## probabilities: 0.490 0.510
## left son=12 (25 obs) right son=13 (24 obs)
## Primary splits:
## price < 2.5 to the right, improve=1.239796, (0 missing)
##
## Node number 7: 30 observations
## predicted class=Y expected loss=0.2 P(node) =0.05016722
## class counts: 6 24
## probabilities: 0.200 0.800
##
## Node number 12: 25 observations
## predicted class=N expected loss=0.4 P(node) =0.04180602
## class counts: 15 10
## probabilities: 0.600 0.400
##
## Node number 13: 24 observations
## predicted class=Y expected loss=0.375 P(node) =0.04013378
## class counts: 9 15
## probabilities: 0.375 0.625
##
## n= 598
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 598 97 N (0.83779264 0.16220736)
## 2) price< 1.5 519 48 N (0.90751445 0.09248555) *
## 3) price>=1.5 79 30 Y (0.37974684 0.62025316)
## 6) price< 5.5 49 24 Y (0.48979592 0.51020408)
## 12) price>=2.5 25 10 N (0.60000000 0.40000000) *
## 13) price< 2.5 24 9 Y (0.37500000 0.62500000) *
## 7) price>=5.5 30 6 Y (0.20000000 0.80000000) *
## Reference
## Prediction N Y
## N 0 0
## Y 501 97
## responsive.fctr responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 501
## 2 97
## Reference
## Prediction N Y
## N 471 48
## Y 30 49
## responsive.fctr responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 471
## 2 Y 48
## responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 30
## 2 49
## Reference
## Prediction N Y
## N 471 48
## Y 30 49
## responsive.fctr responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 471
## 2 Y 48
## responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 30
## 2 49
## Reference
## Prediction N Y
## N 471 48
## Y 30 49
## responsive.fctr responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 471
## 2 Y 48
## responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 30
## 2 49
## Reference
## Prediction N Y
## N 486 58
## Y 15 39
## responsive.fctr responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 486
## 2 Y 58
## responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 15
## 2 39
## Reference
## Prediction N Y
## N 486 58
## Y 15 39
## responsive.fctr responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 486
## 2 Y 58
## responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 15
## 2 39
## Reference
## Prediction N Y
## N 486 58
## Y 15 39
## responsive.fctr responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 486
## 2 Y 58
## responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 15
## 2 39
## Reference
## Prediction N Y
## N 495 73
## Y 6 24
## responsive.fctr responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 495
## 2 Y 73
## responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 6
## 2 24
## Reference
## Prediction N Y
## N 495 73
## Y 6 24
## responsive.fctr responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 495
## 2 Y 73
## responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 6
## 2 24
## Reference
## Prediction N Y
## N 501 97
## Y 0 0
## responsive.fctr responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 501
## 2 Y 97
## responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 0
## 2 0
## Reference
## Prediction N Y
## N 501 97
## Y 0 0
## responsive.fctr responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 501
## 2 Y 97
## responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2791367
## 2 0.1 0.5568182
## 3 0.2 0.5568182
## 4 0.3 0.5568182
## 5 0.4 0.5165563
## 6 0.5 0.5165563
## 7 0.6 0.5165563
## 8 0.7 0.3779528
## 9 0.8 0.3779528
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.fit"
## responsive.fctr responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 471
## 2 Y 48
## responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 30
## 2 49
## Reference
## Prediction N Y
## N 471 48
## Y 30 49
## responsive.fctr responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 471
## 2 Y 48
## responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 30
## 2 49
## Prediction
## Reference N Y
## N 471 30
## Y 48 49
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.86956522 0.48128378 0.83990364 0.89551415 0.83779264
## AccuracyPValue McnemarPValue
## 0.01800816 0.05424550
## Reference
## Prediction N Y
## N 0 0
## Y 215 42
## responsive.fctr responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 215
## 2 42
## Reference
## Prediction N Y
## N 196 20
## Y 19 22
## responsive.fctr responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 196
## 2 Y 20
## responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 19
## 2 22
## Reference
## Prediction N Y
## N 196 20
## Y 19 22
## responsive.fctr responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 196
## 2 Y 20
## responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 19
## 2 22
## Reference
## Prediction N Y
## N 196 20
## Y 19 22
## responsive.fctr responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 196
## 2 Y 20
## responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 19
## 2 22
## Reference
## Prediction N Y
## N 204 26
## Y 11 16
## responsive.fctr responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 204
## 2 Y 26
## responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 11
## 2 16
## Reference
## Prediction N Y
## N 204 26
## Y 11 16
## responsive.fctr responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 204
## 2 Y 26
## responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 11
## 2 16
## Reference
## Prediction N Y
## N 204 26
## Y 11 16
## responsive.fctr responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 204
## 2 Y 26
## responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 11
## 2 16
## Reference
## Prediction N Y
## N 210 29
## Y 5 13
## responsive.fctr responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 210
## 2 Y 29
## responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 5
## 2 13
## Reference
## Prediction N Y
## N 210 29
## Y 5 13
## responsive.fctr responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 210
## 2 Y 29
## responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 5
## 2 13
## Reference
## Prediction N Y
## N 215 42
## Y 0 0
## responsive.fctr responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 215
## 2 Y 42
## responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 0
## 2 0
## Reference
## Prediction N Y
## N 215 42
## Y 0 0
## responsive.fctr responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 215
## 2 Y 42
## responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2809365
## 2 0.1 0.5301205
## 3 0.2 0.5301205
## 4 0.3 0.5301205
## 5 0.4 0.4637681
## 6 0.5 0.4637681
## 7 0.6 0.4637681
## 8 0.7 0.4333333
## 9 0.8 0.4333333
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.OOB"
## responsive.fctr responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 196
## 2 Y 20
## responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 19
## 2 22
## Reference
## Prediction N Y
## N 196 20
## Y 19 22
## responsive.fctr responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 196
## 2 Y 20
## responsive.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 19
## 2 22
## Prediction
## Reference N Y
## N 196 19
## Y 20 22
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.8482490 0.4396489 0.7984412 0.8898113 0.8365759
## AccuracyPValue McnemarPValue
## 0.3423972 1.0000000
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.cv.0.cp.0.rpart rpart price 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.544 0.012 0.7284092
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.3 0.5568182 0.8695652
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8399036 0.8955142 0.4812838 0.7246401
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.3 0.5301205 0.848249
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7984412 0.8898113 0.4396489if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.rpart"
## [1] " indep_vars: price"
## + Fold1: cp=0
## - Fold1: cp=0
## + Fold2: cp=0
## - Fold2: cp=0
## + Fold3: cp=0
## - Fold3: cp=0
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0 on full training set## Warning in myfit_mdl(model_id = "Max.cor.Y", model_method = "rpart",
## model_type = glb_model_type, : model's bestTune found at an extreme of
## tuneGrid for parameter: cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 598
##
## CP nsplit rel error
## 1 0.1958763 0 1.0000000
## 2 0.0257732 1 0.8041237
## 3 0.0000000 3 0.7525773
##
## Variable importance
## price
## 100
##
## Node number 1: 598 observations, complexity param=0.1958763
## predicted class=N expected loss=0.1622074 P(node) =1
## class counts: 501 97
## probabilities: 0.838 0.162
## left son=2 (519 obs) right son=3 (79 obs)
## Primary splits:
## price < 1.5 to the left, improve=38.1952, (0 missing)
##
## Node number 2: 519 observations
## predicted class=N expected loss=0.09248555 P(node) =0.867893
## class counts: 471 48
## probabilities: 0.908 0.092
##
## Node number 3: 79 observations, complexity param=0.0257732
## predicted class=Y expected loss=0.3797468 P(node) =0.132107
## class counts: 30 49
## probabilities: 0.380 0.620
## left son=6 (49 obs) right son=7 (30 obs)
## Primary splits:
## price < 5.5 to the left, improve=3.125394, (0 missing)
##
## Node number 6: 49 observations, complexity param=0.0257732
## predicted class=Y expected loss=0.4897959 P(node) =0.0819398
## class counts: 24 25
## probabilities: 0.490 0.510
## left son=12 (25 obs) right son=13 (24 obs)
## Primary splits:
## price < 2.5 to the right, improve=1.239796, (0 missing)
##
## Node number 7: 30 observations
## predicted class=Y expected loss=0.2 P(node) =0.05016722
## class counts: 6 24
## probabilities: 0.200 0.800
##
## Node number 12: 25 observations
## predicted class=N expected loss=0.4 P(node) =0.04180602
## class counts: 15 10
## probabilities: 0.600 0.400
##
## Node number 13: 24 observations
## predicted class=Y expected loss=0.375 P(node) =0.04013378
## class counts: 9 15
## probabilities: 0.375 0.625
##
## n= 598
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 598 97 N (0.83779264 0.16220736)
## 2) price< 1.5 519 48 N (0.90751445 0.09248555) *
## 3) price>=1.5 79 30 Y (0.37974684 0.62025316)
## 6) price< 5.5 49 24 Y (0.48979592 0.51020408)
## 12) price>=2.5 25 10 N (0.60000000 0.40000000) *
## 13) price< 2.5 24 9 Y (0.37500000 0.62500000) *
## 7) price>=5.5 30 6 Y (0.20000000 0.80000000) *
## Reference
## Prediction N Y
## N 0 0
## Y 501 97
## responsive.fctr responsive.fctr.predict.Max.cor.Y.rpart.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Max.cor.Y.rpart.Y
## 1 501
## 2 97
## Reference
## Prediction N Y
## N 471 48
## Y 30 49
## responsive.fctr responsive.fctr.predict.Max.cor.Y.rpart.N
## 1 N 471
## 2 Y 48
## responsive.fctr.predict.Max.cor.Y.rpart.Y
## 1 30
## 2 49
## Reference
## Prediction N Y
## N 471 48
## Y 30 49
## responsive.fctr responsive.fctr.predict.Max.cor.Y.rpart.N
## 1 N 471
## 2 Y 48
## responsive.fctr.predict.Max.cor.Y.rpart.Y
## 1 30
## 2 49
## Reference
## Prediction N Y
## N 471 48
## Y 30 49
## responsive.fctr responsive.fctr.predict.Max.cor.Y.rpart.N
## 1 N 471
## 2 Y 48
## responsive.fctr.predict.Max.cor.Y.rpart.Y
## 1 30
## 2 49
## Reference
## Prediction N Y
## N 486 58
## Y 15 39
## responsive.fctr responsive.fctr.predict.Max.cor.Y.rpart.N
## 1 N 486
## 2 Y 58
## responsive.fctr.predict.Max.cor.Y.rpart.Y
## 1 15
## 2 39
## Reference
## Prediction N Y
## N 486 58
## Y 15 39
## responsive.fctr responsive.fctr.predict.Max.cor.Y.rpart.N
## 1 N 486
## 2 Y 58
## responsive.fctr.predict.Max.cor.Y.rpart.Y
## 1 15
## 2 39
## Reference
## Prediction N Y
## N 486 58
## Y 15 39
## responsive.fctr responsive.fctr.predict.Max.cor.Y.rpart.N
## 1 N 486
## 2 Y 58
## responsive.fctr.predict.Max.cor.Y.rpart.Y
## 1 15
## 2 39
## Reference
## Prediction N Y
## N 495 73
## Y 6 24
## responsive.fctr responsive.fctr.predict.Max.cor.Y.rpart.N
## 1 N 495
## 2 Y 73
## responsive.fctr.predict.Max.cor.Y.rpart.Y
## 1 6
## 2 24
## Reference
## Prediction N Y
## N 495 73
## Y 6 24
## responsive.fctr responsive.fctr.predict.Max.cor.Y.rpart.N
## 1 N 495
## 2 Y 73
## responsive.fctr.predict.Max.cor.Y.rpart.Y
## 1 6
## 2 24
## Reference
## Prediction N Y
## N 501 97
## Y 0 0
## responsive.fctr responsive.fctr.predict.Max.cor.Y.rpart.N
## 1 N 501
## 2 Y 97
## responsive.fctr.predict.Max.cor.Y.rpart.Y
## 1 0
## 2 0
## Reference
## Prediction N Y
## N 501 97
## Y 0 0
## responsive.fctr responsive.fctr.predict.Max.cor.Y.rpart.N
## 1 N 501
## 2 Y 97
## responsive.fctr.predict.Max.cor.Y.rpart.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2791367
## 2 0.1 0.5568182
## 3 0.2 0.5568182
## 4 0.3 0.5568182
## 5 0.4 0.5165563
## 6 0.5 0.5165563
## 7 0.6 0.5165563
## 8 0.7 0.3779528
## 9 0.8 0.3779528
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.fit"
## responsive.fctr responsive.fctr.predict.Max.cor.Y.rpart.N
## 1 N 471
## 2 Y 48
## responsive.fctr.predict.Max.cor.Y.rpart.Y
## 1 30
## 2 49
## Reference
## Prediction N Y
## N 471 48
## Y 30 49
## responsive.fctr responsive.fctr.predict.Max.cor.Y.rpart.N
## 1 N 471
## 2 Y 48
## responsive.fctr.predict.Max.cor.Y.rpart.Y
## 1 30
## 2 49
## Prediction
## Reference N Y
## N 471 30
## Y 48 49
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.86956522 0.48128378 0.83990364 0.89551415 0.83779264
## AccuracyPValue McnemarPValue
## 0.01800816 0.05424550
## Reference
## Prediction N Y
## N 0 0
## Y 215 42
## responsive.fctr responsive.fctr.predict.Max.cor.Y.rpart.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Max.cor.Y.rpart.Y
## 1 215
## 2 42
## Reference
## Prediction N Y
## N 196 20
## Y 19 22
## responsive.fctr responsive.fctr.predict.Max.cor.Y.rpart.N
## 1 N 196
## 2 Y 20
## responsive.fctr.predict.Max.cor.Y.rpart.Y
## 1 19
## 2 22
## Reference
## Prediction N Y
## N 196 20
## Y 19 22
## responsive.fctr responsive.fctr.predict.Max.cor.Y.rpart.N
## 1 N 196
## 2 Y 20
## responsive.fctr.predict.Max.cor.Y.rpart.Y
## 1 19
## 2 22
## Reference
## Prediction N Y
## N 196 20
## Y 19 22
## responsive.fctr responsive.fctr.predict.Max.cor.Y.rpart.N
## 1 N 196
## 2 Y 20
## responsive.fctr.predict.Max.cor.Y.rpart.Y
## 1 19
## 2 22
## Reference
## Prediction N Y
## N 204 26
## Y 11 16
## responsive.fctr responsive.fctr.predict.Max.cor.Y.rpart.N
## 1 N 204
## 2 Y 26
## responsive.fctr.predict.Max.cor.Y.rpart.Y
## 1 11
## 2 16
## Reference
## Prediction N Y
## N 204 26
## Y 11 16
## responsive.fctr responsive.fctr.predict.Max.cor.Y.rpart.N
## 1 N 204
## 2 Y 26
## responsive.fctr.predict.Max.cor.Y.rpart.Y
## 1 11
## 2 16
## Reference
## Prediction N Y
## N 204 26
## Y 11 16
## responsive.fctr responsive.fctr.predict.Max.cor.Y.rpart.N
## 1 N 204
## 2 Y 26
## responsive.fctr.predict.Max.cor.Y.rpart.Y
## 1 11
## 2 16
## Reference
## Prediction N Y
## N 210 29
## Y 5 13
## responsive.fctr responsive.fctr.predict.Max.cor.Y.rpart.N
## 1 N 210
## 2 Y 29
## responsive.fctr.predict.Max.cor.Y.rpart.Y
## 1 5
## 2 13
## Reference
## Prediction N Y
## N 210 29
## Y 5 13
## responsive.fctr responsive.fctr.predict.Max.cor.Y.rpart.N
## 1 N 210
## 2 Y 29
## responsive.fctr.predict.Max.cor.Y.rpart.Y
## 1 5
## 2 13
## Reference
## Prediction N Y
## N 215 42
## Y 0 0
## responsive.fctr responsive.fctr.predict.Max.cor.Y.rpart.N
## 1 N 215
## 2 Y 42
## responsive.fctr.predict.Max.cor.Y.rpart.Y
## 1 0
## 2 0
## Reference
## Prediction N Y
## N 215 42
## Y 0 0
## responsive.fctr responsive.fctr.predict.Max.cor.Y.rpart.N
## 1 N 215
## 2 Y 42
## responsive.fctr.predict.Max.cor.Y.rpart.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2809365
## 2 0.1 0.5301205
## 3 0.2 0.5301205
## 4 0.3 0.5301205
## 5 0.4 0.4637681
## 6 0.5 0.4637681
## 7 0.6 0.4637681
## 8 0.7 0.4333333
## 9 0.8 0.4333333
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.OOB"
## responsive.fctr responsive.fctr.predict.Max.cor.Y.rpart.N
## 1 N 196
## 2 Y 20
## responsive.fctr.predict.Max.cor.Y.rpart.Y
## 1 19
## 2 22
## Reference
## Prediction N Y
## N 196 20
## Y 19 22
## responsive.fctr responsive.fctr.predict.Max.cor.Y.rpart.N
## 1 N 196
## 2 Y 20
## responsive.fctr.predict.Max.cor.Y.rpart.Y
## 1 19
## 2 22
## Prediction
## Reference N Y
## N 196 19
## Y 20 22
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.8482490 0.4396489 0.7984412 0.8898113 0.8365759
## AccuracyPValue McnemarPValue
## 0.3423972 1.0000000
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.rpart rpart price 3
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 1.021 0.012 0.7284092
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.3 0.5568182 0.8779397
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8399036 0.8955142 0.4523024 0.7246401
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.3 0.5301205 0.848249
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7984412 0.8898113 0.4396489
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.007320661 0.04546369# Used to compare vs. Interactions.High.cor.Y
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.glm"
## [1] " indep_vars: price"
## + Fold1: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## - Fold1: parameter=none
## + Fold2: parameter=none
## - Fold2: parameter=none
## + Fold3: parameter=none
## - Fold3: parameter=none
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.1536 -0.4803 -0.4803 -0.4803 2.1057
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.10173 0.13702 -15.339 < 2e-16 ***
## price 0.39263 0.06131 6.404 1.52e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 530.20 on 597 degrees of freedom
## Residual deviance: 441.64 on 596 degrees of freedom
## AIC: 445.64
##
## Number of Fisher Scoring iterations: 6
## Reference
## Prediction N Y
## N 0 0
## Y 501 97
## responsive.fctr responsive.fctr.predict.Max.cor.Y.glm.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Max.cor.Y.glm.Y
## 1 501
## 2 97
## Reference
## Prediction N Y
## N 0 0
## Y 501 97
## responsive.fctr responsive.fctr.predict.Max.cor.Y.glm.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Max.cor.Y.glm.Y
## 1 501
## 2 97
## Reference
## Prediction N Y
## N 471 48
## Y 30 49
## responsive.fctr responsive.fctr.predict.Max.cor.Y.glm.N
## 1 N 471
## 2 Y 48
## responsive.fctr.predict.Max.cor.Y.glm.Y
## 1 30
## 2 49
## Reference
## Prediction N Y
## N 488 66
## Y 13 31
## responsive.fctr responsive.fctr.predict.Max.cor.Y.glm.N
## 1 N 488
## 2 Y 66
## responsive.fctr.predict.Max.cor.Y.glm.Y
## 1 13
## 2 31
## Reference
## Prediction N Y
## N 493 71
## Y 8 26
## responsive.fctr responsive.fctr.predict.Max.cor.Y.glm.N
## 1 N 493
## 2 Y 71
## responsive.fctr.predict.Max.cor.Y.glm.Y
## 1 8
## 2 26
## Reference
## Prediction N Y
## N 495 73
## Y 6 24
## responsive.fctr responsive.fctr.predict.Max.cor.Y.glm.N
## 1 N 495
## 2 Y 73
## responsive.fctr.predict.Max.cor.Y.glm.Y
## 1 6
## 2 24
## Reference
## Prediction N Y
## N 496 77
## Y 5 20
## responsive.fctr responsive.fctr.predict.Max.cor.Y.glm.N
## 1 N 496
## 2 Y 77
## responsive.fctr.predict.Max.cor.Y.glm.Y
## 1 5
## 2 20
## Reference
## Prediction N Y
## N 496 80
## Y 5 17
## responsive.fctr responsive.fctr.predict.Max.cor.Y.glm.N
## 1 N 496
## 2 Y 80
## responsive.fctr.predict.Max.cor.Y.glm.Y
## 1 5
## 2 17
## Reference
## Prediction N Y
## N 498 82
## Y 3 15
## responsive.fctr responsive.fctr.predict.Max.cor.Y.glm.N
## 1 N 498
## 2 Y 82
## responsive.fctr.predict.Max.cor.Y.glm.Y
## 1 3
## 2 15
## Reference
## Prediction N Y
## N 499 85
## Y 2 12
## responsive.fctr responsive.fctr.predict.Max.cor.Y.glm.N
## 1 N 499
## 2 Y 85
## responsive.fctr.predict.Max.cor.Y.glm.Y
## 1 2
## 2 12
## Reference
## Prediction N Y
## N 501 97
## Y 0 0
## responsive.fctr responsive.fctr.predict.Max.cor.Y.glm.N
## 1 N 501
## 2 Y 97
## responsive.fctr.predict.Max.cor.Y.glm.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2791367
## 2 0.1 0.2791367
## 3 0.2 0.5568182
## 4 0.3 0.4397163
## 5 0.4 0.3969466
## 6 0.5 0.3779528
## 7 0.6 0.3278689
## 8 0.7 0.2857143
## 9 0.8 0.2608696
## 10 0.9 0.2162162
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.2000 to maximize f.score.fit"
## responsive.fctr responsive.fctr.predict.Max.cor.Y.glm.N
## 1 N 471
## 2 Y 48
## responsive.fctr.predict.Max.cor.Y.glm.Y
## 1 30
## 2 49
## Reference
## Prediction N Y
## N 471 48
## Y 30 49
## responsive.fctr responsive.fctr.predict.Max.cor.Y.glm.N
## 1 N 471
## 2 Y 48
## responsive.fctr.predict.Max.cor.Y.glm.Y
## 1 30
## 2 49
## Prediction
## Reference N Y
## N 471 30
## Y 48 49
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.86956522 0.48128378 0.83990364 0.89551415 0.83779264
## AccuracyPValue McnemarPValue
## 0.01800816 0.05424550
## Reference
## Prediction N Y
## N 0 0
## Y 215 42
## responsive.fctr responsive.fctr.predict.Max.cor.Y.glm.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Max.cor.Y.glm.Y
## 1 215
## 2 42
## Reference
## Prediction N Y
## N 0 0
## Y 215 42
## responsive.fctr responsive.fctr.predict.Max.cor.Y.glm.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Max.cor.Y.glm.Y
## 1 215
## 2 42
## Reference
## Prediction N Y
## N 196 20
## Y 19 22
## responsive.fctr responsive.fctr.predict.Max.cor.Y.glm.N
## 1 N 196
## 2 Y 20
## responsive.fctr.predict.Max.cor.Y.glm.Y
## 1 19
## 2 22
## Reference
## Prediction N Y
## N 206 26
## Y 9 16
## responsive.fctr responsive.fctr.predict.Max.cor.Y.glm.N
## 1 N 206
## 2 Y 26
## responsive.fctr.predict.Max.cor.Y.glm.Y
## 1 9
## 2 16
## Reference
## Prediction N Y
## N 209 28
## Y 6 14
## responsive.fctr responsive.fctr.predict.Max.cor.Y.glm.N
## 1 N 209
## 2 Y 28
## responsive.fctr.predict.Max.cor.Y.glm.Y
## 1 6
## 2 14
## Reference
## Prediction N Y
## N 210 29
## Y 5 13
## responsive.fctr responsive.fctr.predict.Max.cor.Y.glm.N
## 1 N 210
## 2 Y 29
## responsive.fctr.predict.Max.cor.Y.glm.Y
## 1 5
## 2 13
## Reference
## Prediction N Y
## N 212 31
## Y 3 11
## responsive.fctr responsive.fctr.predict.Max.cor.Y.glm.N
## 1 N 212
## 2 Y 31
## responsive.fctr.predict.Max.cor.Y.glm.Y
## 1 3
## 2 11
## Reference
## Prediction N Y
## N 212 31
## Y 3 11
## responsive.fctr responsive.fctr.predict.Max.cor.Y.glm.N
## 1 N 212
## 2 Y 31
## responsive.fctr.predict.Max.cor.Y.glm.Y
## 1 3
## 2 11
## Reference
## Prediction N Y
## N 212 32
## Y 3 10
## responsive.fctr responsive.fctr.predict.Max.cor.Y.glm.N
## 1 N 212
## 2 Y 32
## responsive.fctr.predict.Max.cor.Y.glm.Y
## 1 3
## 2 10
## Reference
## Prediction N Y
## N 212 35
## Y 3 7
## responsive.fctr responsive.fctr.predict.Max.cor.Y.glm.N
## 1 N 212
## 2 Y 35
## responsive.fctr.predict.Max.cor.Y.glm.Y
## 1 3
## 2 7
## Reference
## Prediction N Y
## N 215 42
## Y 0 0
## responsive.fctr responsive.fctr.predict.Max.cor.Y.glm.N
## 1 N 215
## 2 Y 42
## responsive.fctr.predict.Max.cor.Y.glm.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2809365
## 2 0.1 0.2809365
## 3 0.2 0.5301205
## 4 0.3 0.4776119
## 5 0.4 0.4516129
## 6 0.5 0.4333333
## 7 0.6 0.3928571
## 8 0.7 0.3928571
## 9 0.8 0.3636364
## 10 0.9 0.2692308
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.2000 to maximize f.score.OOB"
## responsive.fctr responsive.fctr.predict.Max.cor.Y.glm.N
## 1 N 196
## 2 Y 20
## responsive.fctr.predict.Max.cor.Y.glm.Y
## 1 19
## 2 22
## Reference
## Prediction N Y
## N 196 20
## Y 19 22
## responsive.fctr responsive.fctr.predict.Max.cor.Y.glm.N
## 1 N 196
## 2 Y 20
## responsive.fctr.predict.Max.cor.Y.glm.Y
## 1 19
## 2 22
## Prediction
## Reference N Y
## N 196 19
## Y 20 22
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.8482490 0.4396489 0.7984412 0.8898113 0.8365759
## AccuracyPValue McnemarPValue
## 0.3423972 1.0000000
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.glm glm price 1
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.905 0.013 0.7433484
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.2 0.5568182 0.8679062
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8399036 0.8955142 0.3332546 0.8167774
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.2 0.5301205 0.848249
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7984412 0.8898113 0.4396489 445.6427
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.007293418 0.05256359# Interactions.High.cor.Y
if (length(int_feats <- setdiff(unique(glb_feats_df$cor.high.X), NA)) > 0) {
# if (nrow(int_feats_df <- subset(glb_feats_df, !is.na(cor.high.X) &
# (exclude.as.feat == 0))) > 0) {
# lm & glm handle interaction terms; rpart & rf do not
# This does not work - why ???
# indep_vars_vctr <- ifelse(glb_is_binomial,
# c(max_cor_y_x_var, paste(max_cor_y_x_var,
# subset(glb_feats_df, is.na(cor.low))[, "id"], sep=":")),
# union(max_cor_y_x_var, subset(glb_feats_df, is.na(cor.low))[, "id"]))
if (glb_is_regression || glb_is_binomial) {
indep_vars_vctr <-
c(max_cor_y_x_var, paste(max_cor_y_x_var, int_feats, sep=":"))
} else { indep_vars_vctr <- union(max_cor_y_x_var, int_feats) }
ret_lst <- myfit_mdl(model_id="Interact.High.cor.y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
} ## [1] "fitting model: Interact.High.cor.y.glm"
## [1] " indep_vars: price, price:southern, price:summer, price:X2000, price:order, price:public, price:million, price:jone, price:said, price:system, price:busi, price:gas, price:take, price:bid, price:trade, price:higher, price:face, price:littl, price:includ, price:lower, price:make, price:sinc, price:drive, price:inc, price:news, price:base, price:rais, price:dollar, price:trader, price:result, price:grow, price:real, price:avoid, price:percent, price:construct, price:also, price:much, price:tariff, price:allow, price:need, price:everi, price:get, price:month, price:inform, price:now, price:submit, price:privat, price:just, price:X2001, price:dasovich, price:member, price:long, price:materi, price:offici, price:otherwis, price:davi, price:south, price:use, price:law, price:home, price:thing, price:improv, price:juli, price:practic, price:intend, price:futur, price:seek, price:combin, price:though, price:put, price:york, price:complet, price:cynthia, price:say, price:court, price:realli, price:peopl, price:water, price:depart, price:late, price:data, price:risk, price:committe, price:limit, price:protect, price:firm, price:mean, price:let, price:global, price:smith"
## + Fold1: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold1: parameter=none
## + Fold2: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold2: parameter=none
## + Fold3: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold3: parameter=none
## Aggregating results
## Fitting final model on full training set## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -8.49 0.00 0.00 0.00 8.49
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.797e+15 3.040e+06 -920095084 <2e-16 ***
## price 1.092e+15 9.261e+06 117910270 <2e-16 ***
## `price:southern` -3.207e+15 2.708e+07 -118427877 <2e-16 ***
## `price:summer` 1.943e+15 1.985e+07 97865842 <2e-16 ***
## `price:X2000` 3.174e+14 8.362e+06 37956682 <2e-16 ***
## `price:order` 5.237e+14 1.009e+07 51909133 <2e-16 ***
## `price:public` 7.534e+13 1.515e+07 4974001 <2e-16 ***
## `price:million` 9.287e+13 1.248e+07 7439797 <2e-16 ***
## `price:jone` -4.214e+13 1.284e+07 -3282557 <2e-16 ***
## `price:said` 1.455e+14 5.523e+06 26350462 <2e-16 ***
## `price:system` 2.509e+15 1.840e+07 136332030 <2e-16 ***
## `price:busi` -5.876e+14 1.585e+07 -37080270 <2e-16 ***
## `price:gas` -2.631e+13 2.865e+06 -9182216 <2e-16 ***
## `price:take` 4.661e+14 9.560e+06 48756742 <2e-16 ***
## `price:bid` 3.114e+14 9.268e+06 33601167 <2e-16 ***
## `price:trade` 2.061e+14 5.516e+06 37363638 <2e-16 ***
## `price:higher` -3.874e+14 2.575e+07 -15042219 <2e-16 ***
## `price:face` -2.964e+15 4.796e+07 -61804071 <2e-16 ***
## `price:littl` 2.726e+15 4.902e+07 55608072 <2e-16 ***
## `price:includ` -1.277e+14 7.361e+06 -17353768 <2e-16 ***
## `price:lower` -1.377e+15 2.321e+07 -59337759 <2e-16 ***
## `price:make` -4.943e+14 1.099e+07 -44983309 <2e-16 ***
## `price:sinc` 2.139e+14 1.636e+07 13074998 <2e-16 ***
## `price:drive` -9.549e+14 1.962e+07 -48669738 <2e-16 ***
## `price:inc` -2.353e+15 2.007e+07 -117207172 <2e-16 ***
## `price:news` -4.761e+14 1.849e+07 -25745493 <2e-16 ***
## `price:base` -1.478e+15 1.814e+07 -81500335 <2e-16 ***
## `price:rais` 3.025e+15 5.367e+07 56350576 <2e-16 ***
## `price:dollar` 6.575e+14 2.407e+07 27312449 <2e-16 ***
## `price:trader` 1.915e+15 3.194e+07 59950591 <2e-16 ***
## `price:result` 3.163e+15 2.210e+07 143157904 <2e-16 ***
## `price:grow` -1.880e+15 4.284e+07 -43879372 <2e-16 ***
## `price:real` -2.540e+14 8.347e+06 -30427122 <2e-16 ***
## `price:avoid` -8.499e+14 5.649e+07 -15045051 <2e-16 ***
## `price:percent` 2.072e+14 1.836e+07 11284679 <2e-16 ***
## `price:construct` -2.573e+15 3.963e+07 -64910052 <2e-16 ***
## `price:also` 6.348e+14 1.031e+07 61550644 <2e-16 ***
## `price:much` -1.092e+15 1.998e+07 -54659744 <2e-16 ***
## `price:tariff` -7.081e+15 7.163e+07 -98844577 <2e-16 ***
## `price:allow` -5.707e+14 2.156e+07 -26468313 <2e-16 ***
## `price:need` 5.817e+14 7.004e+06 83042096 <2e-16 ***
## `price:everi` 1.199e+15 1.762e+07 68068474 <2e-16 ***
## `price:get` -5.568e+14 8.720e+06 -63845413 <2e-16 ***
## `price:month` -1.851e+14 7.880e+06 -23490308 <2e-16 ***
## `price:inform` -4.719e+14 7.661e+06 -61601144 <2e-16 ***
## `price:now` 2.832e+15 2.295e+07 123396919 <2e-16 ***
## `price:submit` -1.110e+15 1.883e+07 -58915405 <2e-16 ***
## `price:privat` -3.329e+15 3.129e+07 -106416152 <2e-16 ***
## `price:just` -9.524e+14 1.530e+07 -62235011 <2e-16 ***
## `price:X2001` 3.849e+14 7.606e+06 50613585 <2e-16 ***
## `price:dasovich` 1.982e+15 1.734e+07 114293099 <2e-16 ***
## `price:member` -1.825e+15 1.884e+07 -96887513 <2e-16 ***
## `price:long` 1.913e+15 2.873e+07 66577368 <2e-16 ***
## `price:materi` 1.283e+15 3.448e+07 37207896 <2e-16 ***
## `price:offici` -1.813e+14 1.886e+07 -9613409 <2e-16 ***
## `price:otherwis` -2.390e+15 3.094e+07 -77228458 <2e-16 ***
## `price:davi` 3.790e+14 8.875e+06 42700351 <2e-16 ***
## `price:south` -2.767e+15 1.921e+07 -144081944 <2e-16 ***
## `price:use` -1.118e+14 6.159e+06 -18151640 <2e-16 ***
## `price:law` -3.530e+15 2.585e+07 -136522286 <2e-16 ***
## `price:home` 7.750e+15 5.635e+07 137533978 <2e-16 ***
## `price:thing` 9.502e+14 2.774e+07 34253389 <2e-16 ***
## `price:improv` 7.012e+15 4.669e+07 150173265 <2e-16 ***
## `price:juli` -1.594e+15 3.476e+07 -45854398 <2e-16 ***
## `price:practic` 3.880e+15 4.693e+07 82682567 <2e-16 ***
## `price:intend` -1.656e+14 2.687e+07 -6163602 <2e-16 ***
## `price:futur` -4.710e+14 9.853e+06 -47806086 <2e-16 ***
## `price:seek` 4.893e+15 4.832e+07 101251142 <2e-16 ***
## `price:combin` -1.118e+15 4.595e+07 -24323658 <2e-16 ***
## `price:though` -2.171e+15 5.770e+07 -37622631 <2e-16 ***
## `price:put` -8.208e+14 1.935e+07 -42423158 <2e-16 ***
## `price:york` -1.082e+15 3.641e+07 -29707987 <2e-16 ***
## `price:complet` 1.221e+14 3.221e+07 3790756 <2e-16 ***
## `price:cynthia` -1.745e+15 2.846e+07 -61323421 <2e-16 ***
## `price:say` -6.868e+14 1.367e+07 -50231803 <2e-16 ***
## `price:court` -2.244e+14 1.153e+07 -19454783 <2e-16 ***
## `price:realli` 2.052e+15 4.143e+07 49530834 <2e-16 ***
## `price:peopl` -5.180e+13 3.732e+07 -1387892 <2e-16 ***
## `price:water` 4.337e+14 5.138e+07 8439911 <2e-16 ***
## `price:depart` 3.622e+14 2.429e+07 14912090 <2e-16 ***
## `price:late` -1.090e+14 3.012e+07 -3618720 <2e-16 ***
## `price:data` 8.031e+14 1.462e+07 54924678 <2e-16 ***
## `price:risk` -3.542e+14 7.471e+06 -47411371 <2e-16 ***
## `price:committe` -1.011e+15 3.943e+07 -25630374 <2e-16 ***
## `price:limit` 1.306e+14 1.722e+07 7584385 <2e-16 ***
## `price:protect` -2.604e+15 3.437e+07 -75751196 <2e-16 ***
## `price:firm` 1.765e+13 8.500e+06 2077026 <2e-16 ***
## `price:mean` -4.481e+15 3.748e+07 -119569538 <2e-16 ***
## `price:let` -2.187e+14 1.166e+07 -18760763 <2e-16 ***
## `price:global` -1.299e+15 3.363e+07 -38625031 <2e-16 ***
## `price:smith` -3.209e+15 2.844e+07 -112827243 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 530.2 on 597 degrees of freedom
## Residual deviance: 3316.0 on 507 degrees of freedom
## AIC: 3498
##
## Number of Fisher Scoring iterations: 12
## Reference
## Prediction N Y
## N 0 0
## Y 501 97
## responsive.fctr responsive.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Interact.High.cor.y.glm.Y
## 1 501
## 2 97
## Reference
## Prediction N Y
## N 500 45
## Y 1 52
## responsive.fctr responsive.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 500
## 2 Y 45
## responsive.fctr.predict.Interact.High.cor.y.glm.Y
## 1 1
## 2 52
## Reference
## Prediction N Y
## N 500 45
## Y 1 52
## responsive.fctr responsive.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 500
## 2 Y 45
## responsive.fctr.predict.Interact.High.cor.y.glm.Y
## 1 1
## 2 52
## Reference
## Prediction N Y
## N 500 45
## Y 1 52
## responsive.fctr responsive.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 500
## 2 Y 45
## responsive.fctr.predict.Interact.High.cor.y.glm.Y
## 1 1
## 2 52
## Reference
## Prediction N Y
## N 500 45
## Y 1 52
## responsive.fctr responsive.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 500
## 2 Y 45
## responsive.fctr.predict.Interact.High.cor.y.glm.Y
## 1 1
## 2 52
## Reference
## Prediction N Y
## N 500 45
## Y 1 52
## responsive.fctr responsive.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 500
## 2 Y 45
## responsive.fctr.predict.Interact.High.cor.y.glm.Y
## 1 1
## 2 52
## Reference
## Prediction N Y
## N 500 45
## Y 1 52
## responsive.fctr responsive.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 500
## 2 Y 45
## responsive.fctr.predict.Interact.High.cor.y.glm.Y
## 1 1
## 2 52
## Reference
## Prediction N Y
## N 500 45
## Y 1 52
## responsive.fctr responsive.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 500
## 2 Y 45
## responsive.fctr.predict.Interact.High.cor.y.glm.Y
## 1 1
## 2 52
## Reference
## Prediction N Y
## N 500 45
## Y 1 52
## responsive.fctr responsive.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 500
## 2 Y 45
## responsive.fctr.predict.Interact.High.cor.y.glm.Y
## 1 1
## 2 52
## Reference
## Prediction N Y
## N 500 45
## Y 1 52
## responsive.fctr responsive.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 500
## 2 Y 45
## responsive.fctr.predict.Interact.High.cor.y.glm.Y
## 1 1
## 2 52
## Reference
## Prediction N Y
## N 501 97
## Y 0 0
## responsive.fctr responsive.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 501
## 2 Y 97
## responsive.fctr.predict.Interact.High.cor.y.glm.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2791367
## 2 0.1 0.6933333
## 3 0.2 0.6933333
## 4 0.3 0.6933333
## 5 0.4 0.6933333
## 6 0.5 0.6933333
## 7 0.6 0.6933333
## 8 0.7 0.6933333
## 9 0.8 0.6933333
## 10 0.9 0.6933333
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.9000 to maximize f.score.fit"
## responsive.fctr responsive.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 500
## 2 Y 45
## responsive.fctr.predict.Interact.High.cor.y.glm.Y
## 1 1
## 2 52
## Reference
## Prediction N Y
## N 500 45
## Y 1 52
## responsive.fctr responsive.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 500
## 2 Y 45
## responsive.fctr.predict.Interact.High.cor.y.glm.Y
## 1 1
## 2 52
## Prediction
## Reference N Y
## N 500 1
## Y 45 52
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.230769e-01 6.536302e-01 8.987265e-01 9.431349e-01 8.377926e-01
## AccuracyPValue McnemarPValue
## 4.889189e-10 2.297590e-10
## Reference
## Prediction N Y
## N 0 0
## Y 215 42
## responsive.fctr responsive.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Interact.High.cor.y.glm.Y
## 1 215
## 2 42
## Reference
## Prediction N Y
## N 205 27
## Y 10 15
## responsive.fctr responsive.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 205
## 2 Y 27
## responsive.fctr.predict.Interact.High.cor.y.glm.Y
## 1 10
## 2 15
## Reference
## Prediction N Y
## N 205 27
## Y 10 15
## responsive.fctr responsive.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 205
## 2 Y 27
## responsive.fctr.predict.Interact.High.cor.y.glm.Y
## 1 10
## 2 15
## Reference
## Prediction N Y
## N 205 27
## Y 10 15
## responsive.fctr responsive.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 205
## 2 Y 27
## responsive.fctr.predict.Interact.High.cor.y.glm.Y
## 1 10
## 2 15
## Reference
## Prediction N Y
## N 205 27
## Y 10 15
## responsive.fctr responsive.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 205
## 2 Y 27
## responsive.fctr.predict.Interact.High.cor.y.glm.Y
## 1 10
## 2 15
## Reference
## Prediction N Y
## N 205 27
## Y 10 15
## responsive.fctr responsive.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 205
## 2 Y 27
## responsive.fctr.predict.Interact.High.cor.y.glm.Y
## 1 10
## 2 15
## Reference
## Prediction N Y
## N 205 27
## Y 10 15
## responsive.fctr responsive.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 205
## 2 Y 27
## responsive.fctr.predict.Interact.High.cor.y.glm.Y
## 1 10
## 2 15
## Reference
## Prediction N Y
## N 205 27
## Y 10 15
## responsive.fctr responsive.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 205
## 2 Y 27
## responsive.fctr.predict.Interact.High.cor.y.glm.Y
## 1 10
## 2 15
## Reference
## Prediction N Y
## N 205 27
## Y 10 15
## responsive.fctr responsive.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 205
## 2 Y 27
## responsive.fctr.predict.Interact.High.cor.y.glm.Y
## 1 10
## 2 15
## Reference
## Prediction N Y
## N 205 27
## Y 10 15
## responsive.fctr responsive.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 205
## 2 Y 27
## responsive.fctr.predict.Interact.High.cor.y.glm.Y
## 1 10
## 2 15
## Reference
## Prediction N Y
## N 215 42
## Y 0 0
## responsive.fctr responsive.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 215
## 2 Y 42
## responsive.fctr.predict.Interact.High.cor.y.glm.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2809365
## 2 0.1 0.4477612
## 3 0.2 0.4477612
## 4 0.3 0.4477612
## 5 0.4 0.4477612
## 6 0.5 0.4477612
## 7 0.6 0.4477612
## 8 0.7 0.4477612
## 9 0.8 0.4477612
## 10 0.9 0.4477612
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.9000 to maximize f.score.OOB"
## responsive.fctr responsive.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 205
## 2 Y 27
## responsive.fctr.predict.Interact.High.cor.y.glm.Y
## 1 10
## 2 15
## Reference
## Prediction N Y
## N 205 27
## Y 10 15
## responsive.fctr responsive.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 205
## 2 Y 27
## responsive.fctr.predict.Interact.High.cor.y.glm.Y
## 1 10
## 2 15
## Prediction
## Reference N Y
## N 205 10
## Y 27 15
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.856031128 0.371056287 0.807058834 0.896563950 0.836575875
## AccuracyPValue McnemarPValue
## 0.226296773 0.008528852
## model_id model_method
## 1 Interact.High.cor.y.glm glm
## feats
## 1 price, price:southern, price:summer, price:X2000, price:order, price:public, price:million, price:jone, price:said, price:system, price:busi, price:gas, price:take, price:bid, price:trade, price:higher, price:face, price:littl, price:includ, price:lower, price:make, price:sinc, price:drive, price:inc, price:news, price:base, price:rais, price:dollar, price:trader, price:result, price:grow, price:real, price:avoid, price:percent, price:construct, price:also, price:much, price:tariff, price:allow, price:need, price:everi, price:get, price:month, price:inform, price:now, price:submit, price:privat, price:just, price:X2001, price:dasovich, price:member, price:long, price:materi, price:offici, price:otherwis, price:davi, price:south, price:use, price:law, price:home, price:thing, price:improv, price:juli, price:practic, price:intend, price:futur, price:seek, price:combin, price:though, price:put, price:york, price:complet, price:cynthia, price:say, price:court, price:realli, price:peopl, price:water, price:depart, price:late, price:data, price:risk, price:committe, price:limit, price:protect, price:firm, price:mean, price:let, price:global, price:smith
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.695 0.227
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.7670432 0.9 0.6933333 0.836139
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8987265 0.9431349 0.2451495 0.6553156
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.9 0.4477612 0.8560311
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.8070588 0.896564 0.3710563 3498.016
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.009968522 0.04576649# Low.cor.X
if (glb_is_classification && glb_is_binomial)
indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) &
is.ConditionalX.y &
(exclude.as.feat != 1))[, "id"] else
indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) &
(exclude.as.feat != 1))[, "id"]
ret_lst <- myfit_mdl(model_id="Low.cor.X",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Low.cor.X.glm"
## [1] " indep_vars: independ, suppli, emerg, summer, custom, transmiss, iso, jone, forc, gas, longer, low, increas, expect, problem, consum, pay, plant, higher, rate, cap, commiss, sourc, solut, three, among, right, addit, author, line, energi, bring, push, major, load, told, lower, public, pipelin, peak, supplier, without, electr, hour, interconnect, econom, two, implement, analyst, june, close, establish, want, within, purchas, announc, enough, servic, inc, grid, provid, regulatori, news, steffesnaenronenron, creat, propos, still, situat, profit, X2000, whether, transport, paid, fall, action, serv, avail, today, full, unit, face, pass, compani, requir, cut, point, step, andor, earlier, design, past, pacif, sell, mani, reserv, one, nation, consid, billion, percent, deregul, industri, money, key, presid, day, set, remain, issu, success, plan, edison, call, accord, last, sever, own, earli, appear, base, balanc, get, staff, anoth, deliveri, sinc, got, control, small, articl, per, washington, dollar, better, seller, benefit, board, larg, wednesday, help, near, sold, term, construct, five, like, around, amount, direct, top, structur, governor, signific, meet, end, friday, report, fact, dont, agenc, offer, follow, file, taken, purpos, opinion, talk, certain, grow, good, keep, altern, busi, dasovichnaenron, exist, come, make, real, pge, monday, effort, month, back, repres, measur, determin, exchang, develop, lead, need, januari, payment, immedi, least, run, explain, messag, havent, next., tuesday, occur, san, receiv, program, upon, start, refer, ago, approxim, differ, releas, legisl, analysi, just, later, open, chairman, region, move, clear, administr, forward, post, director, decemb, produc, believ, estim, alreadi, fix, found, recent, tri, consult, facil, trader, see, novemb, cpuc, richard, far, entir, investig, polici, retail, indic, free, execut, respons, modifi, X100, citi, X2001, steven, third, cover, oblig, long, portion, august, suggest, basi, idea, return, possibl, tariff, procedur, might, person, readi, condit, agre, though, due, four, combin, X1999, big, someth, comput, look, averag, replac, everi, member, press, record, applic, focus, hold, short, decis, becom, jame, connect, corp, posit, coupl, howev, court, manag, potenti, littl, thursday, made, put, detail, begin, answer, site, well, must, total, excess, effect, enter, involv, complet, chanc, togeth, negoti, therefor, advanc, notic, ensur, brian, except, morn, give, act, storag, accept, coordin, unless, confer, otherwis, publish, share, fail, web, given, arrang, that, statement, project, soon, current, second, question, first, week, william, ask, commod, say, hope, schedul, intend, jim, reach, less, case, outsid, daili, section, instead, name, place, rule, guarante, address, govern, associ, practic, subject, lot, access, trade, locat, susan, model, invest, main, depart, financ, note, left, identifi, internet, reflect, actual, express, support, import, qualiti, confidenti, view, show, comment, peopl, eric, correct, work, similar, assum, fund, dasovich, late, sign, seem, seen, cash, option, tell, add, word, commerci, flow, sale, attorney, previous, concern, submit, karen, approach, data, necessari, decid, various, recommend, abl, along, water, physic, claim, final, error, number, event, think, paul, alan, april, parti, exampl, els, group, anyon, dissemin, distribut, onlin, realli, south, delet, sue, law, individu, oil, prepar, hear, leav, advis, head, cynthia, joe, notifi, speak, thought, jan, tim, awar, strong, page, mean, sender, activ, organ, east, present, protect, corpor, west, kevin, enron, initi, singl, area, relat, kind, everyon, firm, electron, extend, assist, affect, octob, origin, specif, attent, septemb, deliv, chang, york, rob, hard, north, valu, termin, capit, offic, either, calcul, yet, ill, consider, side, approv, request, shall, asset, product, feel, let, carol, someon, march, probabl, sent, commit, appropri, matter, depend, research, chris, keannaenronenron, quick, contain, hand, gari, respect, steve, guy, date, deal, tomorrow, repli, latest, interest, extens, scott, financi, liquid, texa, local, intern, entiti, vinc, anyth, team, read, great, via, risk, impact, proceed, desk, ive, done, harri, llc, afternoon, mari, michael, cours, best, perform, link, delay, account, committe, opportun, privileg, process, find, sure, mail, greg, send, legal, recipi, london, transact, confirm, mention, summari, convers, prior, visit, respond, bit, yesterday, websit, format, review, paper, georg, peter, handl, kelli, item, employe, net, provis, februari, suit, frank, robert, attend, strategi, dave, email.1, street, join, dan, list, ken, met, appreci, draft, contact, status, bob, version, type, appli, sorri, mark, credit, understand, kaminskihouect, etc, rick, brief, communic, document, bank, sheet, phone, gerald, ena, copi, fyi, counsel, john, updat, jeffrey, roger, lesli, lisa, agreement, volum, letter, elizabeth, .rnorm, settlement, discuss, houston, global, info, transfer, pleas, america, smith, stephani, memo, dear, regard, fax, form, counterparti, languag, mike, master, X77002, david, check, print, amend, eol, kate, attach, book, revis, thank"
## + Fold1: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold1: parameter=none
## + Fold2: parameter=none## Warning: glm.fit: algorithm did not converge## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold2: parameter=none
## + Fold3: parameter=none## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold3: parameter=none
## Aggregating results
## Fitting final model on full training set## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: not plotting observations with leverage one:
## 1, 3, 4, 6, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19, 22, 23, 26, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 99, 100, 101, 102, 103, 104, 105, 107, 108, 109, 110, 111, 112, 113, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 387, 388, 389, 390, 391, 392, 393, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 409, 411, 412, 413, 414, 415, 417, 418, 419, 421, 422, 423, 425, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598
## Warning: not plotting observations with leverage one:
## 1, 3, 4, 6, 8, 9, 11, 12, 13, 14, 16, 17, 18, 19, 22, 23, 26, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 99, 100, 101, 102, 103, 104, 105, 107, 108, 109, 110, 111, 112, 113, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 387, 388, 389, 390, 391, 392, 393, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 409, 411, 412, 413, 414, 415, 417, 418, 419, 421, 422, 423, 425, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced##
## Call:
## NULL
##
## Deviance Residuals:
## 1 2 3 4 5 6 8 11 12 16
## 0.00 -8.49 0.00 -8.49 -8.49 0.00 -8.49 -8.49 -8.49 -8.49
## 18 19 21 23 24 25 28 30 31 34
## -8.49 0.00 0.00 0.00 0.00 0.00 -8.49 0.00 0.00 0.00
## 37 39 41 42 43 45 46 49 50 51
## 0.00 0.00 0.00 0.00 0.00 -8.49 8.49 0.00 0.00 -8.49
## 52 53 54 55 56 57 58 59 60 61
## -8.49 0.00 -8.49 0.00 -8.49 0.00 -8.49 0.00 0.00 0.00
## 62 63 64 65 66 67 68 70 73 74
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -8.49 0.00 8.49
## 75 77 78 79 80 82 83 85 86 87
## 0.00 -8.49 0.00 8.49 0.00 8.49 0.00 0.00 0.00 0.00
## 88 89 90 93 94 95 96 97 98 100
## 0.00 0.00 -8.49 0.00 0.00 0.00 0.00 0.00 8.49 0.00
## 101 103 104 105 107 108 113 117 118 119
## 0.00 0.00 0.00 0.00 0.00 8.49 0.00 0.00 0.00 0.00
## 121 122 123 124 125 126 127 130 133 135
## 0.00 0.00 0.00 0.00 -8.49 0.00 0.00 0.00 0.00 0.00
## 137 138 139 140 141 142 143 144 145 146
## 0.00 0.00 0.00 0.00 0.00 -8.49 0.00 0.00 0.00 8.49
## 147 148 149 150 151 154 155 156 157 159
## 0.00 0.00 0.00 0.00 8.49 0.00 0.00 0.00 0.00 0.00
## 160 162 165 166 167 168 169 171 172 173
## 0.00 0.00 0.00 -8.49 0.00 0.00 0.00 0.00 0.00 8.49
## 175 176 178 179 181 182 183 184 185 186
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 187 188 190 191 192 194 196 197 198 199
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 200 203 205 206 207 210 211 212 213 214
## 0.00 0.00 0.00 8.49 0.00 0.00 0.00 -8.49 0.00 0.00
## 215 216 217 218 221 223 224 225 226 228
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 229 231 232 234 235 237 238 239 240 242
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 244 245 246 247 250 251 252 254 256 257
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 258 261 262 263 264 265 267 268 271 272
## 8.49 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 273 274 277 278 279 281 283 284 285 287
## 0.00 8.49 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 290 291 292 293 294 295 298 302 305 306
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 307 308 309 310 311 312 313 316 317 318
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 322 323 324 325 326 327 328 329 331 332
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 8.49 0.00
## 333 334 335 338 339 340 341 342 343 345
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 346 348 349 350 352 353 354 357 358 360
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 361 363 364 365 367 368 369 370 372 373
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 374 375 377 378 379 380 381 382 383 384
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 385 386 387 388 390 391 392 394 395 396
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 398 399 400 401 402 404 405 406 409 411
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 414 417 418 420 421 422 423 424 426 428
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 429 430 432 433 434 438 439 440 441 443
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 444 446 447 448 449 450 451 453 454 455
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 457 458 459 460 461 462 463 465 467 468
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 469 471 474 477 478 479 480 482 483 484
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 485 488 489 490 491 499 501 502 503 504
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 505 509 510 512 513 514 516 517 518 519
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 520 521 523 524 525 526 527 528 529 530
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 531 533 536 537 538 539 540 541 542 543
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 545 546 548 549 550 551 552 553 555 556
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 557 558 560 561 563 564 565 566 567 569
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 570 571 573 575 576 577 578 580 581 582
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 583 584 586 587 588 590 593 594 595 596
## 0.00 8.49 0.00 0.00 8.49 0.00 0.00 0.00 8.49 0.00
## 597 598 599 600 601 602 603 604 605 607
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 608 609 612 614 616 618 619 620 621 622
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 624 627 628 629 630 631 632 633 634 636
## 0.00 0.00 -8.49 0.00 8.49 0.00 0.00 0.00 0.00 0.00
## 637 639 640 642 643 644 645 646 647 648
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 649 650 652 653 654 655 656 657 658 660
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 8.49 0.00
## 662 663 665 666 667 668 669 671 672 673
## 0.00 0.00 0.00 0.00 8.49 0.00 0.00 8.49 0.00 0.00
## 674 675 677 678 679 683 684 685 686 687
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 693 694 695 696 698 700 701 704 705 706
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 707 709 710 711 713 714 715 716 717 718
## 0.00 0.00 0.00 0.00 0.00 8.49 0.00 0.00 0.00 0.00
## 719 720 721 723 726 728 730 733 735 736
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 737 740 741 743 745 746 748 751 752 754
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 755 759 761 763 764 765 766 767 768 770
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 771 772 774 775 776 777 778 780 781 782
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 783 784 785 786 789 790 791 793 794 797
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 798 801 802 804 806 807 809 811 812 813
## 0.00 0.00 -8.49 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 814 819 820 821 822 825 828 830 831 832
## 0.00 0.00 0.00 8.49 0.00 8.49 0.00 0.00 -8.49 0.00
## 834 835 836 838 839 840 841 842 844 845
## -8.49 0.00 0.00 0.00 8.49 0.00 0.00 0.00 0.00 0.00
## 847 848 849 850 851 852 853 855
## 0.00 0.00 0.00 0.00 0.00 -8.49 0.00 0.00
##
## Coefficients: (101 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -8.327e+14 7.880e+07 -10567931 <2e-16 ***
## independ 2.186e+28 1.143e+22 1912206 <2e-16 ***
## suppli 1.857e+28 9.710e+21 1912206 <2e-16 ***
## emerg -6.719e+28 3.514e+22 -1912206 <2e-16 ***
## summer 7.539e+27 3.943e+21 1912206 <2e-16 ***
## custom 1.873e+27 9.796e+20 1912206 <2e-16 ***
## transmiss 8.831e+27 4.618e+21 1912206 <2e-16 ***
## iso -8.726e+27 4.563e+21 -1912206 <2e-16 ***
## jone 8.168e+27 4.272e+21 1912206 <2e-16 ***
## forc 1.647e+28 8.611e+21 1912206 <2e-16 ***
## gas 1.689e+28 8.832e+21 1912206 <2e-16 ***
## longer 4.331e+28 2.265e+22 1912206 <2e-16 ***
## low 1.703e+27 8.907e+20 1912206 <2e-16 ***
## increas -9.029e+28 4.722e+22 -1912206 <2e-16 ***
## expect 4.561e+28 2.385e+22 1912206 <2e-16 ***
## problem -2.609e+28 1.364e+22 -1912206 <2e-16 ***
## consum 6.966e+27 3.643e+21 1912206 <2e-16 ***
## pay 9.215e+27 4.819e+21 1912206 <2e-16 ***
## plant 4.078e+27 2.132e+21 1912206 <2e-16 ***
## higher 3.296e+28 1.723e+22 1912206 <2e-16 ***
## rate 2.528e+28 1.322e+22 1912206 <2e-16 ***
## cap -4.631e+25 2.422e+19 -1912206 <2e-16 ***
## commiss 1.183e+28 6.189e+21 1912206 <2e-16 ***
## sourc -3.014e+27 1.576e+21 -1912206 <2e-16 ***
## solut 4.591e+27 2.401e+21 1912206 <2e-16 ***
## three -7.994e+28 4.180e+22 -1912206 <2e-16 ***
## among -5.194e+28 2.716e+22 -1912206 <2e-16 ***
## right 1.220e+28 6.381e+21 1912206 <2e-16 ***
## addit -8.498e+26 4.444e+20 -1912206 <2e-16 ***
## author 8.931e+27 4.670e+21 1912206 <2e-16 ***
## line -4.687e+28 2.451e+22 -1912206 <2e-16 ***
## energi -8.050e+27 4.210e+21 -1912206 <2e-16 ***
## bring -1.655e+28 8.657e+21 -1912206 <2e-16 ***
## push -6.317e+28 3.303e+22 -1912206 <2e-16 ***
## major -1.563e+29 8.175e+22 -1912206 <2e-16 ***
## load 1.413e+28 7.388e+21 1912206 <2e-16 ***
## told -1.454e+29 7.606e+22 -1912206 <2e-16 ***
## lower 8.205e+28 4.291e+22 1912206 <2e-16 ***
## public 1.445e+27 7.557e+20 1912206 <2e-16 ***
## pipelin 1.325e+28 6.931e+21 1912206 <2e-16 ***
## peak 2.868e+28 1.500e+22 1912206 <2e-16 ***
## supplier 5.153e+27 2.695e+21 1912206 <2e-16 ***
## without -5.072e+28 2.652e+22 -1912206 <2e-16 ***
## electr 1.942e+27 1.016e+21 1912206 <2e-16 ***
## hour 1.564e+28 8.180e+21 1912206 <2e-16 ***
## interconnect -6.429e+27 3.362e+21 -1912206 <2e-16 ***
## econom -6.607e+27 3.455e+21 -1912206 <2e-16 ***
## two 3.531e+28 1.847e+22 1912206 <2e-16 ***
## implement 2.310e+28 1.208e+22 1912206 <2e-16 ***
## analyst -1.224e+28 6.403e+21 -1912206 <2e-16 ***
## june -3.493e+27 1.827e+21 -1912206 <2e-16 ***
## close 1.916e+28 1.002e+22 1912206 <2e-16 ***
## establish 1.049e+29 5.487e+22 1912206 <2e-16 ***
## want 5.020e+27 2.625e+21 1912206 <2e-16 ***
## within -6.457e+26 3.376e+20 -1912206 <2e-16 ***
## purchas -3.154e+27 1.649e+21 -1912206 <2e-16 ***
## announc -1.493e+28 7.808e+21 -1912206 <2e-16 ***
## enough -1.492e+28 7.804e+21 -1912206 <2e-16 ***
## servic -5.602e+25 2.929e+19 -1912206 <2e-16 ***
## inc 4.151e+27 2.171e+21 1912206 <2e-16 ***
## grid 4.852e+28 2.537e+22 1912206 <2e-16 ***
## provid -2.154e+28 1.127e+22 -1912206 <2e-16 ***
## regulatori 4.661e+28 2.438e+22 1912206 <2e-16 ***
## news 1.866e+28 9.760e+21 1912206 <2e-16 ***
## steffesnaenronenron -1.803e+28 9.431e+21 -1912206 <2e-16 ***
## creat 7.811e+28 4.085e+22 1912206 <2e-16 ***
## propos -5.185e+27 2.712e+21 -1912206 <2e-16 ***
## still -2.030e+28 1.061e+22 -1912206 <2e-16 ***
## situat 1.200e+28 6.277e+21 1912206 <2e-16 ***
## profit -8.109e+28 4.241e+22 -1912206 <2e-16 ***
## X2000 3.640e+27 1.904e+21 1912206 <2e-16 ***
## whether 1.665e+28 8.706e+21 1912206 <2e-16 ***
## transport -1.834e+28 9.590e+21 -1912206 <2e-16 ***
## paid -3.125e+28 1.634e+22 -1912206 <2e-16 ***
## fall -2.029e+29 1.061e+23 -1912206 <2e-16 ***
## action -3.050e+28 1.595e+22 -1912206 <2e-16 ***
## serv -9.847e+27 5.150e+21 -1912206 <2e-16 ***
## avail -3.487e+28 1.823e+22 -1912206 <2e-16 ***
## today -4.167e+15 1.009e+08 -41309732 <2e-16 ***
## full 9.837e+27 5.144e+21 1912206 <2e-16 ***
## unit 4.633e+28 2.423e+22 1912206 <2e-16 ***
## face 4.286e+28 2.241e+22 1912206 <2e-16 ***
## pass 3.980e+28 2.082e+22 1912206 <2e-16 ***
## compani -1.896e+28 9.917e+21 -1912206 <2e-16 ***
## requir -4.392e+27 2.297e+21 -1912206 <2e-16 ***
## cut -1.262e+28 6.599e+21 -1912206 <2e-16 ***
## point -9.379e+27 4.905e+21 -1912206 <2e-16 ***
## step -6.114e+28 3.197e+22 -1912206 <2e-16 ***
## andor 1.778e+28 9.300e+21 1912206 <2e-16 ***
## earlier -1.887e+28 9.870e+21 -1912206 <2e-16 ***
## design -9.552e+26 4.995e+20 -1912206 <2e-16 ***
## past -1.938e+16 4.640e+08 -41771959 <2e-16 ***
## pacif 5.192e+27 2.715e+21 1912206 <2e-16 ***
## sell 3.129e+27 1.636e+21 1912206 <2e-16 ***
## mani 4.743e+28 2.480e+22 1912206 <2e-16 ***
## reserv 5.763e+28 3.014e+22 1912206 <2e-16 ***
## one -1.501e+27 7.848e+20 -1912206 <2e-16 ***
## nation -5.469e+27 2.860e+21 -1912206 <2e-16 ***
## consid -7.221e+28 3.776e+22 -1912206 <2e-16 ***
## billion -3.054e+28 1.597e+22 -1912206 <2e-16 ***
## percent 3.771e+27 1.972e+21 1912206 <2e-16 ***
## deregul 5.337e+28 2.791e+22 1912206 <2e-16 ***
## industri -3.590e+16 3.686e+08 -97408799 <2e-16 ***
## money -4.184e+27 2.188e+21 -1912206 <2e-16 ***
## key -3.522e+28 1.842e+22 -1912206 <2e-16 ***
## presid -2.885e+28 1.509e+22 -1912206 <2e-16 ***
## day 1.026e+27 5.366e+20 1912206 <2e-16 ***
## set 3.922e+28 2.051e+22 1912206 <2e-16 ***
## remain -2.118e+28 1.108e+22 -1912206 <2e-16 ***
## issu -8.036e+27 4.203e+21 -1912206 <2e-16 ***
## success 2.119e+28 1.108e+22 1912206 <2e-16 ***
## plan -3.180e+28 1.663e+22 -1912206 <2e-16 ***
## edison -3.347e+28 1.751e+22 -1912206 <2e-16 ***
## call 7.267e+27 3.800e+21 1912206 <2e-16 ***
## accord 6.297e+28 3.293e+22 1912206 <2e-16 ***
## last 1.379e+28 7.212e+21 1912206 <2e-16 ***
## sever 5.835e+28 3.052e+22 1912206 <2e-16 ***
## own 7.811e+28 4.085e+22 1912206 <2e-16 ***
## earli -1.227e+29 6.418e+22 -1912206 <2e-16 ***
## appear 5.817e+28 3.042e+22 1912206 <2e-16 ***
## base -4.113e+28 2.151e+22 -1912206 <2e-16 ***
## balanc -6.091e+28 3.186e+22 -1912206 <2e-16 ***
## get 4.519e+27 2.363e+21 1912206 <2e-16 ***
## staff -1.798e+27 9.405e+20 -1912206 <2e-16 ***
## anoth 8.345e+28 4.364e+22 1912206 <2e-16 ***
## deliveri -1.455e+15 1.759e+08 -8272300 <2e-16 ***
## sinc 1.454e+28 7.603e+21 1912206 <2e-16 ***
## got -1.620e+28 8.470e+21 -1912206 <2e-16 ***
## control 5.962e+27 3.118e+21 1912206 <2e-16 ***
## small -3.419e+27 1.788e+21 -1912206 <2e-16 ***
## articl 2.062e+28 1.078e+22 1912206 <2e-16 ***
## per 6.994e+14 1.154e+08 6058204 <2e-16 ***
## washington -1.049e+29 5.486e+22 -1912206 <2e-16 ***
## dollar 4.347e+28 2.273e+22 1912206 <2e-16 ***
## better 4.304e+26 2.251e+20 1912206 <2e-16 ***
## seller -1.520e+28 7.948e+21 -1912206 <2e-16 ***
## benefit 5.783e+28 3.024e+22 1912206 <2e-16 ***
## board -5.565e+28 2.910e+22 -1912206 <2e-16 ***
## larg -2.957e+28 1.546e+22 -1912206 <2e-16 ***
## wednesday -7.145e+25 3.736e+19 -1912206 <2e-16 ***
## help 1.347e+28 7.047e+21 1912206 <2e-16 ***
## near 1.450e+29 7.585e+22 1912206 <2e-16 ***
## sold 2.786e+28 1.457e+22 1912206 <2e-16 ***
## term 1.613e+28 8.437e+21 1912206 <2e-16 ***
## construct -2.967e+28 1.552e+22 -1912206 <2e-16 ***
## five 9.377e+28 4.904e+22 1912206 <2e-16 ***
## like -7.267e+27 3.800e+21 -1912206 <2e-16 ***
## around 6.166e+28 3.225e+22 1912206 <2e-16 ***
## amount -9.066e+27 4.741e+21 -1912206 <2e-16 ***
## direct -6.543e+27 3.422e+21 -1912206 <2e-16 ***
## top -8.864e+28 4.635e+22 -1912206 <2e-16 ***
## structur 1.543e+28 8.067e+21 1912206 <2e-16 ***
## governor 3.364e+28 1.759e+22 1912206 <2e-16 ***
## signific 8.343e+27 4.363e+21 1912206 <2e-16 ***
## meet -3.274e+28 1.712e+22 -1912206 <2e-16 ***
## end -1.236e+28 6.464e+21 -1912206 <2e-16 ***
## friday 4.870e+25 2.547e+19 1912206 <2e-16 ***
## report 1.034e+16 2.654e+08 38957906 <2e-16 ***
## fact 4.555e+28 2.382e+22 1912206 <2e-16 ***
## dont 2.159e+27 1.129e+21 1912206 <2e-16 ***
## agenc 3.589e+28 1.877e+22 1912206 <2e-16 ***
## offer -5.439e+27 2.844e+21 -1912206 <2e-16 ***
## follow 9.801e+27 5.126e+21 1912206 <2e-16 ***
## file 1.700e+27 8.889e+20 1912206 <2e-16 ***
## taken 1.746e+28 9.129e+21 1912206 <2e-16 ***
## purpos 7.480e+27 3.912e+21 1912206 <2e-16 ***
## opinion -1.530e+28 8.000e+21 -1912206 <2e-16 ***
## talk 2.116e+27 1.106e+21 1912206 <2e-16 ***
## certain 5.875e+28 3.072e+22 1912206 <2e-16 ***
## grow 8.722e+28 4.561e+22 1912206 <2e-16 ***
## good -4.524e+27 2.366e+21 -1912206 <2e-16 ***
## keep -2.266e+28 1.185e+22 -1912206 <2e-16 ***
## altern -1.334e+29 6.978e+22 -1912206 <2e-16 ***
## busi 6.391e+27 3.342e+21 1912206 <2e-16 ***
## dasovichnaenron -2.044e+28 1.069e+22 -1912206 <2e-16 ***
## exist 3.153e+28 1.649e+22 1912206 <2e-16 ***
## come -3.125e+27 1.634e+21 -1912206 <2e-16 ***
## make 5.387e+27 2.817e+21 1912206 <2e-16 ***
## real -1.946e+28 1.018e+22 -1912206 <2e-16 ***
## pge -1.240e+28 6.483e+21 -1912206 <2e-16 ***
## monday 3.074e+28 1.607e+22 1912206 <2e-16 ***
## effort -6.923e+28 3.620e+22 -1912206 <2e-16 ***
## month -4.960e+28 2.594e+22 -1912206 <2e-16 ***
## back 2.249e+28 1.176e+22 1912206 <2e-16 ***
## repres -5.479e+28 2.865e+22 -1912206 <2e-16 ***
## measur 9.884e+28 5.169e+22 1912206 <2e-16 ***
## determin 3.257e+28 1.703e+22 1912206 <2e-16 ***
## exchang -8.648e+26 4.523e+20 -1912206 <2e-16 ***
## develop -1.509e+28 7.889e+21 -1912206 <2e-16 ***
## lead -1.999e+28 1.045e+22 -1912206 <2e-16 ***
## need -1.008e+28 5.271e+21 -1912206 <2e-16 ***
## januari 4.732e+28 2.475e+22 1912206 <2e-16 ***
## payment 9.378e+27 4.904e+21 1912206 <2e-16 ***
## immedi 5.896e+27 3.083e+21 1912206 <2e-16 ***
## least 1.043e+28 5.452e+21 1912206 <2e-16 ***
## run -1.349e+28 7.055e+21 -1912206 <2e-16 ***
## explain 8.400e+27 4.393e+21 1912206 <2e-16 ***
## messag -7.327e+16 2.533e+09 -28928207 <2e-16 ***
## havent -2.266e+28 1.185e+22 -1912206 <2e-16 ***
## next. -2.830e+28 1.480e+22 -1912206 <2e-16 ***
## tuesday 5.855e+28 3.062e+22 1912206 <2e-16 ***
## occur 7.681e+27 4.017e+21 1912206 <2e-16 ***
## san 1.490e+28 7.793e+21 1912206 <2e-16 ***
## receiv -1.078e+28 5.637e+21 -1912206 <2e-16 ***
## program 1.007e+28 5.267e+21 1912206 <2e-16 ***
## upon 3.126e+27 1.635e+21 1912206 <2e-16 ***
## start 6.970e+27 3.645e+21 1912206 <2e-16 ***
## refer -1.280e+28 6.693e+21 -1912206 <2e-16 ***
## ago 6.801e+27 3.557e+21 1912206 <2e-16 ***
## approxim -4.792e+28 2.506e+22 -1912206 <2e-16 ***
## differ 5.332e+27 2.789e+21 1912206 <2e-16 ***
## releas -1.658e+26 8.668e+19 -1912206 <2e-16 ***
## legisl 7.665e+28 4.008e+22 1912206 <2e-16 ***
## analysi 4.003e+28 2.094e+22 1912206 <2e-16 ***
## just -1.490e+27 7.791e+20 -1912206 <2e-16 ***
## later 1.522e+28 7.959e+21 1912206 <2e-16 ***
## open -1.868e+28 9.767e+21 -1912206 <2e-16 ***
## chairman 1.367e+29 7.149e+22 1912206 <2e-16 ***
## region -2.782e+27 1.455e+21 -1912206 <2e-16 ***
## move 2.542e+28 1.329e+22 1912206 <2e-16 ***
## clear -1.172e+28 6.131e+21 -1912206 <2e-16 ***
## administr 5.188e+28 2.713e+22 1912206 <2e-16 ***
## forward -3.634e+27 1.900e+21 -1912206 <2e-16 ***
## post 1.516e+28 7.927e+21 1912206 <2e-16 ***
## director 1.283e+28 6.710e+21 1912206 <2e-16 ***
## decemb -7.811e+27 4.085e+21 -1912206 <2e-16 ***
## produc 9.827e+27 5.139e+21 1912206 <2e-16 ***
## believ -2.058e+28 1.076e+22 -1912206 <2e-16 ***
## estim 3.380e+28 1.768e+22 1912206 <2e-16 ***
## alreadi -1.108e+28 5.796e+21 -1912206 <2e-16 ***
## fix 3.587e+28 1.876e+22 1912206 <2e-16 ***
## found -3.303e+28 1.727e+22 -1912206 <2e-16 ***
## recent 1.010e+28 5.284e+21 1912206 <2e-16 ***
## tri 8.569e+27 4.481e+21 1912206 <2e-16 ***
## consult -6.591e+28 3.447e+22 -1912206 <2e-16 ***
## facil 5.275e+27 2.759e+21 1912206 <2e-16 ***
## trader -1.543e+28 8.070e+21 -1912206 <2e-16 ***
## see -1.700e+27 8.889e+20 -1912206 <2e-16 ***
## novemb 5.896e+27 3.083e+21 1912206 <2e-16 ***
## cpuc -2.960e+28 1.548e+22 -1912206 <2e-16 ***
## richard 7.424e+27 3.882e+21 1912206 <2e-16 ***
## far -3.925e+28 2.053e+22 -1912206 <2e-16 ***
## entir -4.332e+28 2.265e+22 -1912206 <2e-16 ***
## investig -4.775e+28 2.497e+22 -1912206 <2e-16 ***
## polici -1.877e+28 9.818e+21 -1912206 <2e-16 ***
## retail 3.021e+28 1.580e+22 1912206 <2e-16 ***
## indic 2.230e+28 1.166e+22 1912206 <2e-16 ***
## free 6.334e+27 3.312e+21 1912206 <2e-16 ***
## execut -1.050e+28 5.489e+21 -1912206 <2e-16 ***
## respons -3.708e+27 1.939e+21 -1912206 <2e-16 ***
## modifi 7.411e+28 3.876e+22 1912206 <2e-16 ***
## X100 1.939e+28 1.014e+22 1912206 <2e-16 ***
## citi -1.091e+28 5.706e+21 -1912206 <2e-16 ***
## X2001 -3.645e+27 1.906e+21 -1912206 <2e-16 ***
## steven -2.979e+28 1.558e+22 -1912206 <2e-16 ***
## third -2.844e+28 1.487e+22 -1912206 <2e-16 ***
## cover -5.303e+28 2.773e+22 -1912206 <2e-16 ***
## oblig -1.918e+28 1.003e+22 -1912206 <2e-16 ***
## long -2.112e+28 1.105e+22 -1912206 <2e-16 ***
## portion -6.579e+28 3.441e+22 -1912206 <2e-16 ***
## august -6.236e+28 3.261e+22 -1912206 <2e-16 ***
## suggest 3.014e+28 1.576e+22 1912206 <2e-16 ***
## basi -3.125e+28 1.634e+22 -1912206 <2e-16 ***
## idea 2.028e+29 1.061e+23 1912206 <2e-16 ***
## return 1.383e+27 7.235e+20 1912206 <2e-16 ***
## possibl -1.368e+28 7.153e+21 -1912206 <2e-16 ***
## tariff 9.979e+27 5.219e+21 1912206 <2e-16 ***
## procedur 7.720e+27 4.037e+21 1912206 <2e-16 ***
## might 2.118e+28 1.107e+22 1912206 <2e-16 ***
## person -5.706e+28 2.984e+22 -1912206 <2e-16 ***
## readi -1.105e+28 5.778e+21 -1912206 <2e-16 ***
## condit -8.793e+28 4.598e+22 -1912206 <2e-16 ***
## agre -1.464e+28 7.654e+21 -1912206 <2e-16 ***
## though 6.337e+27 3.314e+21 1912206 <2e-16 ***
## due 2.616e+28 1.368e+22 1912206 <2e-16 ***
## four 6.291e+28 3.290e+22 1912206 <2e-16 ***
## combin -2.516e+28 1.316e+22 -1912206 <2e-16 ***
## X1999 -5.015e+28 2.623e+22 -1912206 <2e-16 ***
## big 2.892e+28 1.512e+22 1912206 <2e-16 ***
## someth 1.539e+28 8.051e+21 1912206 <2e-16 ***
## comput -3.414e+28 1.785e+22 -1912206 <2e-16 ***
## look 4.908e+15 3.128e+08 15693892 <2e-16 ***
## averag 1.391e+28 7.274e+21 1912206 <2e-16 ***
## replac 2.425e+28 1.268e+22 1912206 <2e-16 ***
## everi 4.054e+28 2.120e+22 1912206 <2e-16 ***
## member -4.041e+26 2.113e+20 -1912206 <2e-16 ***
## press -5.532e+28 2.893e+22 -1912206 <2e-16 ***
## record -2.151e+28 1.125e+22 -1912206 <2e-16 ***
## applic 3.433e+28 1.796e+22 1912206 <2e-16 ***
## focus -8.233e+28 4.305e+22 -1912206 <2e-16 ***
## hold 1.120e+28 5.859e+21 1912206 <2e-16 ***
## short -2.063e+28 1.079e+22 -1912206 <2e-16 ***
## decis 4.384e+26 2.293e+20 1912206 <2e-16 ***
## becom 2.783e+28 1.456e+22 1912206 <2e-16 ***
## jame 8.398e+27 4.392e+21 1912206 <2e-16 ***
## connect -8.511e+28 4.451e+22 -1912206 <2e-16 ***
## corp 2.227e+28 1.164e+22 1912206 <2e-16 ***
## posit -7.389e+27 3.864e+21 -1912206 <2e-16 ***
## coupl 4.188e+25 2.190e+19 1912206 <2e-16 ***
## howev 1.289e+28 6.739e+21 1912206 <2e-16 ***
## court 1.616e+28 8.452e+21 1912206 <2e-16 ***
## manag 1.653e+28 8.644e+21 1912206 <2e-16 ***
## potenti 3.522e+28 1.842e+22 1912206 <2e-16 ***
## littl 7.269e+26 3.801e+20 1912206 <2e-16 ***
## thursday 3.928e+27 2.054e+21 1912206 <2e-16 ***
## made 2.009e+28 1.051e+22 1912206 <2e-16 ***
## put -4.469e+28 2.337e+22 -1912206 <2e-16 ***
## detail 9.540e+27 4.989e+21 1912206 <2e-16 ***
## begin -3.119e+28 1.631e+22 -1912206 <2e-16 ***
## answer -2.172e+28 1.136e+22 -1912206 <2e-16 ***
## site 3.303e+28 1.727e+22 1912206 <2e-16 ***
## well 6.614e+27 3.459e+21 1912206 <2e-16 ***
## must 1.321e+28 6.910e+21 1912206 <2e-16 ***
## total -1.255e+28 6.564e+21 -1912206 <2e-16 ***
## excess -2.494e+28 1.304e+22 -1912206 <2e-16 ***
## effect 1.073e+28 5.609e+21 1912206 <2e-16 ***
## enter -7.180e+28 3.755e+22 -1912206 <2e-16 ***
## involv -8.553e+27 4.473e+21 -1912206 <2e-16 ***
## complet 2.008e+27 1.050e+21 1912206 <2e-16 ***
## chanc 2.349e+27 1.228e+21 1912206 <2e-16 ***
## togeth -2.789e+28 1.459e+22 -1912206 <2e-16 ***
## negoti -6.920e+28 3.619e+22 -1912206 <2e-16 ***
## therefor 2.615e+28 1.368e+22 1912206 <2e-16 ***
## advanc -5.423e+28 2.836e+22 -1912206 <2e-16 ***
## notic 1.556e+27 8.137e+20 1912206 <2e-16 ***
## ensur -9.714e+27 5.080e+21 -1912206 <2e-16 ***
## brian -1.248e+28 6.527e+21 -1912206 <2e-16 ***
## except -5.248e+28 2.744e+22 -1912206 <2e-16 ***
## morn -5.208e+27 2.724e+21 -1912206 <2e-16 ***
## give -8.489e+27 4.440e+21 -1912206 <2e-16 ***
## act 2.673e+28 1.398e+22 1912206 <2e-16 ***
## storag 1.164e+28 6.090e+21 1912206 <2e-16 ***
## accept -1.768e+28 9.246e+21 -1912206 <2e-16 ***
## coordin 4.419e+28 2.311e+22 1912206 <2e-16 ***
## unless 4.973e+28 2.601e+22 1912206 <2e-16 ***
## confer 1.769e+28 9.252e+21 1912206 <2e-16 ***
## otherwis 9.951e+27 5.204e+21 1912206 <2e-16 ***
## publish 1.997e+28 1.044e+22 1912206 <2e-16 ***
## share 2.967e+28 1.552e+22 1912206 <2e-16 ***
## fail 5.189e+28 2.713e+22 1912206 <2e-16 ***
## web -3.601e+28 1.883e+22 -1912206 <2e-16 ***
## given 5.867e+28 3.068e+22 1912206 <2e-16 ***
## arrang -8.967e+28 4.689e+22 -1912206 <2e-16 ***
## that 4.141e+28 2.165e+22 1912206 <2e-16 ***
## statement -1.756e+28 9.183e+21 -1912206 <2e-16 ***
## project -2.495e+28 1.305e+22 -1912206 <2e-16 ***
## soon -1.336e+28 6.984e+21 -1912206 <2e-16 ***
## current 1.375e+28 7.191e+21 1912206 <2e-16 ***
## second 3.069e+28 1.605e+22 1912206 <2e-16 ***
## question -3.132e+26 1.638e+20 -1912206 <2e-16 ***
## first -5.352e+26 2.799e+20 -1912206 <2e-16 ***
## week 1.230e+28 6.432e+21 1912206 <2e-16 ***
## william -7.938e+27 4.151e+21 -1912206 <2e-16 ***
## ask -6.293e+26 3.291e+20 -1912206 <2e-16 ***
## commod 1.000e+27 5.232e+20 1912206 <2e-16 ***
## say -3.137e+28 1.641e+22 -1912206 <2e-16 ***
## hope -2.569e+27 1.344e+21 -1912206 <2e-16 ***
## schedul 1.051e+28 5.495e+21 1912206 <2e-16 ***
## intend 9.013e+28 4.713e+22 1912206 <2e-16 ***
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## committe -4.907e+27 2.566e+21 -1912206 <2e-16 ***
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## sure -3.979e+28 2.081e+22 -1912206 <2e-16 ***
## mail 1.177e+26 6.157e+19 1912206 <2e-16 ***
## greg NA NA NA NA
## send NA NA NA NA
## legal -8.113e+15 3.058e+08 -26531214 <2e-16 ***
## recipi NA NA NA NA
## london NA NA NA NA
## transact NA NA NA NA
## confirm NA NA NA NA
## mention NA NA NA NA
## summari NA NA NA NA
## convers NA NA NA NA
## prior NA NA NA NA
## visit NA NA NA NA
## respond NA NA NA NA
## bit NA NA NA NA
## yesterday NA NA NA NA
## websit NA NA NA NA
## format NA NA NA NA
## review NA NA NA NA
## paper NA NA NA NA
## georg NA NA NA NA
## peter NA NA NA NA
## handl NA NA NA NA
## kelli NA NA NA NA
## item NA NA NA NA
## employe NA NA NA NA
## net NA NA NA NA
## provis NA NA NA NA
## februari NA NA NA NA
## suit NA NA NA NA
## frank NA NA NA NA
## robert NA NA NA NA
## attend NA NA NA NA
## strategi NA NA NA NA
## dave NA NA NA NA
## email.1 NA NA NA NA
## street NA NA NA NA
## join NA NA NA NA
## dan NA NA NA NA
## list NA NA NA NA
## ken NA NA NA NA
## met NA NA NA NA
## appreci NA NA NA NA
## draft NA NA NA NA
## contact NA NA NA NA
## status -1.110e+16 2.755e+08 -40311360 <2e-16 ***
## bob -5.110e+15 5.224e+08 -9781340 <2e-16 ***
## version NA NA NA NA
## type NA NA NA NA
## appli NA NA NA NA
## sorri NA NA NA NA
## mark NA NA NA NA
## credit NA NA NA NA
## understand NA NA NA NA
## kaminskihouect NA NA NA NA
## etc NA NA NA NA
## rick NA NA NA NA
## brief NA NA NA NA
## communic NA NA NA NA
## document NA NA NA NA
## bank NA NA NA NA
## sheet NA NA NA NA
## phone NA NA NA NA
## gerald NA NA NA NA
## ena NA NA NA NA
## copi 1.918e+17 5.782e+09 33176914 <2e-16 ***
## fyi NA NA NA NA
## counsel NA NA NA NA
## john NA NA NA NA
## updat NA NA NA NA
## jeffrey NA NA NA NA
## roger NA NA NA NA
## lesli 5.276e+14 9.779e+07 5395864 <2e-16 ***
## lisa NA NA NA NA
## agreement NA NA NA NA
## volum NA NA NA NA
## letter NA NA NA NA
## elizabeth NA NA NA NA
## .rnorm -4.611e+14 2.716e+07 -16977217 <2e-16 ***
## settlement 6.172e+28 3.227e+22 1912206 <2e-16 ***
## discuss NA NA NA NA
## houston NA NA NA NA
## global NA NA NA NA
## info NA NA NA NA
## transfer NA NA NA NA
## pleas NA NA NA NA
## america NA NA NA NA
## smith NA NA NA NA
## stephani NA NA NA NA
## memo NA NA NA NA
## dear NA NA NA NA
## regard NA NA NA NA
## fax NA NA NA NA
## form NA NA NA NA
## counterparti NA NA NA NA
## languag NA NA NA NA
## mike NA NA NA NA
## master NA NA NA NA
## X77002 NA NA NA NA
## david NA NA NA NA
## check NA NA NA NA
## print NA NA NA NA
## amend NA NA NA NA
## eol NA NA NA NA
## kate NA NA NA NA
## attach NA NA NA NA
## book NA NA NA NA
## revis NA NA NA NA
## thank NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 530.2 on 597 degrees of freedom
## Residual deviance: 3676.5 on 4 degrees of freedom
## AIC: 4864.5
##
## Number of Fisher Scoring iterations: 25## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Reference
## Prediction N Y
## N 0 0
## Y 501 97
## responsive.fctr responsive.fctr.predict.Low.cor.X.glm.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Low.cor.X.glm.Y
## 1 501
## 2 97
## Reference
## Prediction N Y
## N 474 24
## Y 27 73
## responsive.fctr responsive.fctr.predict.Low.cor.X.glm.N
## 1 N 474
## 2 Y 24
## responsive.fctr.predict.Low.cor.X.glm.Y
## 1 27
## 2 73
## Reference
## Prediction N Y
## N 474 24
## Y 27 73
## responsive.fctr responsive.fctr.predict.Low.cor.X.glm.N
## 1 N 474
## 2 Y 24
## responsive.fctr.predict.Low.cor.X.glm.Y
## 1 27
## 2 73
## Reference
## Prediction N Y
## N 474 24
## Y 27 73
## responsive.fctr responsive.fctr.predict.Low.cor.X.glm.N
## 1 N 474
## 2 Y 24
## responsive.fctr.predict.Low.cor.X.glm.Y
## 1 27
## 2 73
## Reference
## Prediction N Y
## N 474 24
## Y 27 73
## responsive.fctr responsive.fctr.predict.Low.cor.X.glm.N
## 1 N 474
## 2 Y 24
## responsive.fctr.predict.Low.cor.X.glm.Y
## 1 27
## 2 73
## Reference
## Prediction N Y
## N 474 24
## Y 27 73
## responsive.fctr responsive.fctr.predict.Low.cor.X.glm.N
## 1 N 474
## 2 Y 24
## responsive.fctr.predict.Low.cor.X.glm.Y
## 1 27
## 2 73
## Reference
## Prediction N Y
## N 474 24
## Y 27 73
## responsive.fctr responsive.fctr.predict.Low.cor.X.glm.N
## 1 N 474
## 2 Y 24
## responsive.fctr.predict.Low.cor.X.glm.Y
## 1 27
## 2 73
## Reference
## Prediction N Y
## N 474 24
## Y 27 73
## responsive.fctr responsive.fctr.predict.Low.cor.X.glm.N
## 1 N 474
## 2 Y 24
## responsive.fctr.predict.Low.cor.X.glm.Y
## 1 27
## 2 73
## Reference
## Prediction N Y
## N 474 24
## Y 27 73
## responsive.fctr responsive.fctr.predict.Low.cor.X.glm.N
## 1 N 474
## 2 Y 24
## responsive.fctr.predict.Low.cor.X.glm.Y
## 1 27
## 2 73
## Reference
## Prediction N Y
## N 474 24
## Y 27 73
## responsive.fctr responsive.fctr.predict.Low.cor.X.glm.N
## 1 N 474
## 2 Y 24
## responsive.fctr.predict.Low.cor.X.glm.Y
## 1 27
## 2 73
## Reference
## Prediction N Y
## N 501 97
## Y 0 0
## responsive.fctr responsive.fctr.predict.Low.cor.X.glm.N
## 1 N 501
## 2 Y 97
## responsive.fctr.predict.Low.cor.X.glm.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2791367
## 2 0.1 0.7411168
## 3 0.2 0.7411168
## 4 0.3 0.7411168
## 5 0.4 0.7411168
## 6 0.5 0.7411168
## 7 0.6 0.7411168
## 8 0.7 0.7411168
## 9 0.8 0.7411168
## 10 0.9 0.7411168
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.9000 to maximize f.score.fit"
## responsive.fctr responsive.fctr.predict.Low.cor.X.glm.N
## 1 N 474
## 2 Y 24
## responsive.fctr.predict.Low.cor.X.glm.Y
## 1 27
## 2 73
## Reference
## Prediction N Y
## N 474 24
## Y 27 73
## responsive.fctr responsive.fctr.predict.Low.cor.X.glm.N
## 1 N 474
## 2 Y 24
## responsive.fctr.predict.Low.cor.X.glm.Y
## 1 27
## 2 73
## Prediction
## Reference N Y
## N 474 27
## Y 24 73
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.147157e-01 6.900799e-01 8.893904e-01 9.358425e-01 8.377926e-01
## AccuracyPValue McnemarPValue
## 2.596766e-08 7.794345e-01## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Reference
## Prediction N Y
## N 0 0
## Y 215 42
## responsive.fctr responsive.fctr.predict.Low.cor.X.glm.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Low.cor.X.glm.Y
## 1 215
## 2 42
## Reference
## Prediction N Y
## N 109 25
## Y 106 17
## responsive.fctr responsive.fctr.predict.Low.cor.X.glm.N
## 1 N 109
## 2 Y 25
## responsive.fctr.predict.Low.cor.X.glm.Y
## 1 106
## 2 17
## Reference
## Prediction N Y
## N 109 25
## Y 106 17
## responsive.fctr responsive.fctr.predict.Low.cor.X.glm.N
## 1 N 109
## 2 Y 25
## responsive.fctr.predict.Low.cor.X.glm.Y
## 1 106
## 2 17
## Reference
## Prediction N Y
## N 109 25
## Y 106 17
## responsive.fctr responsive.fctr.predict.Low.cor.X.glm.N
## 1 N 109
## 2 Y 25
## responsive.fctr.predict.Low.cor.X.glm.Y
## 1 106
## 2 17
## Reference
## Prediction N Y
## N 109 25
## Y 106 17
## responsive.fctr responsive.fctr.predict.Low.cor.X.glm.N
## 1 N 109
## 2 Y 25
## responsive.fctr.predict.Low.cor.X.glm.Y
## 1 106
## 2 17
## Reference
## Prediction N Y
## N 109 25
## Y 106 17
## responsive.fctr responsive.fctr.predict.Low.cor.X.glm.N
## 1 N 109
## 2 Y 25
## responsive.fctr.predict.Low.cor.X.glm.Y
## 1 106
## 2 17
## Reference
## Prediction N Y
## N 109 25
## Y 106 17
## responsive.fctr responsive.fctr.predict.Low.cor.X.glm.N
## 1 N 109
## 2 Y 25
## responsive.fctr.predict.Low.cor.X.glm.Y
## 1 106
## 2 17
## Reference
## Prediction N Y
## N 109 25
## Y 106 17
## responsive.fctr responsive.fctr.predict.Low.cor.X.glm.N
## 1 N 109
## 2 Y 25
## responsive.fctr.predict.Low.cor.X.glm.Y
## 1 106
## 2 17
## Reference
## Prediction N Y
## N 109 25
## Y 106 17
## responsive.fctr responsive.fctr.predict.Low.cor.X.glm.N
## 1 N 109
## 2 Y 25
## responsive.fctr.predict.Low.cor.X.glm.Y
## 1 106
## 2 17
## Reference
## Prediction N Y
## N 109 25
## Y 106 17
## responsive.fctr responsive.fctr.predict.Low.cor.X.glm.N
## 1 N 109
## 2 Y 25
## responsive.fctr.predict.Low.cor.X.glm.Y
## 1 106
## 2 17
## Reference
## Prediction N Y
## N 215 42
## Y 0 0
## responsive.fctr responsive.fctr.predict.Low.cor.X.glm.N
## 1 N 215
## 2 Y 42
## responsive.fctr.predict.Low.cor.X.glm.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2809365
## 2 0.1 0.2060606
## 3 0.2 0.2060606
## 4 0.3 0.2060606
## 5 0.4 0.2060606
## 6 0.5 0.2060606
## 7 0.6 0.2060606
## 8 0.7 0.2060606
## 9 0.8 0.2060606
## 10 0.9 0.2060606
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.0000 to maximize f.score.OOB"
## responsive.fctr responsive.fctr.predict.Low.cor.X.glm.Y
## 1 N 215
## 2 Y 42
## Reference
## Prediction N Y
## N 0 0
## Y 215 42
## responsive.fctr responsive.fctr.predict.Low.cor.X.glm.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Low.cor.X.glm.Y
## 1 215
## 2 42
## Prediction
## Reference N Y
## N 0 215
## Y 0 42
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 1.634241e-01 0.000000e+00 1.203919e-01 2.144132e-01 8.365759e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 3.036391e-48
## model_id model_method
## 1 Low.cor.X.glm glm
## feats
## 1 independ, suppli, emerg, summer, custom, transmiss, iso, jone, forc, gas, longer, low, increas, expect, problem, consum, pay, plant, higher, rate, cap, commiss, sourc, solut, three, among, right, addit, author, line, energi, bring, push, major, load, told, lower, public, pipelin, peak, supplier, without, electr, hour, interconnect, econom, two, implement, analyst, june, close, establish, want, within, purchas, announc, enough, servic, inc, grid, provid, regulatori, news, steffesnaenronenron, creat, propos, still, situat, profit, X2000, whether, transport, paid, fall, action, serv, avail, today, full, unit, face, pass, compani, requir, cut, point, step, andor, earlier, design, past, pacif, sell, mani, reserv, one, nation, consid, billion, percent, deregul, industri, money, key, presid, day, set, remain, issu, success, plan, edison, call, accord, last, sever, own, earli, appear, base, balanc, get, staff, anoth, deliveri, sinc, got, control, small, articl, per, washington, dollar, better, seller, benefit, board, larg, wednesday, help, near, sold, term, construct, five, like, around, amount, direct, top, structur, governor, signific, meet, end, friday, report, fact, dont, agenc, offer, follow, file, taken, purpos, opinion, talk, certain, grow, good, keep, altern, busi, dasovichnaenron, exist, come, make, real, pge, monday, effort, month, back, repres, measur, determin, exchang, develop, lead, need, januari, payment, immedi, least, run, explain, messag, havent, next., tuesday, occur, san, receiv, program, upon, start, refer, ago, approxim, differ, releas, legisl, analysi, just, later, open, chairman, region, move, clear, administr, forward, post, director, decemb, produc, believ, estim, alreadi, fix, found, recent, tri, consult, facil, trader, see, novemb, cpuc, richard, far, entir, investig, polici, retail, indic, free, execut, respons, modifi, X100, citi, X2001, steven, third, cover, oblig, long, portion, august, suggest, basi, idea, return, possibl, tariff, procedur, might, person, readi, condit, agre, though, due, four, combin, X1999, big, someth, comput, look, averag, replac, everi, member, press, record, applic, focus, hold, short, decis, becom, jame, connect, corp, posit, coupl, howev, court, manag, potenti, littl, thursday, made, put, detail, begin, answer, site, well, must, total, excess, effect, enter, involv, complet, chanc, togeth, negoti, therefor, advanc, notic, ensur, brian, except, morn, give, act, storag, accept, coordin, unless, confer, otherwis, publish, share, fail, web, given, arrang, that, statement, project, soon, current, second, question, first, week, william, ask, commod, say, hope, schedul, intend, jim, reach, less, case, outsid, daili, section, instead, name, place, rule, guarante, address, govern, associ, practic, subject, lot, access, trade, locat, susan, model, invest, main, depart, financ, note, left, identifi, internet, reflect, actual, express, support, import, qualiti, confidenti, view, show, comment, peopl, eric, correct, work, similar, assum, fund, dasovich, late, sign, seem, seen, cash, option, tell, add, word, commerci, flow, sale, attorney, previous, concern, submit, karen, approach, data, necessari, decid, various, recommend, abl, along, water, physic, claim, final, error, number, event, think, paul, alan, april, parti, exampl, els, group, anyon, dissemin, distribut, onlin, realli, south, delet, sue, law, individu, oil, prepar, hear, leav, advis, head, cynthia, joe, notifi, speak, thought, jan, tim, awar, strong, page, mean, sender, activ, organ, east, present, protect, corpor, west, kevin, enron, initi, singl, area, relat, kind, everyon, firm, electron, extend, assist, affect, octob, origin, specif, attent, septemb, deliv, chang, york, rob, hard, north, valu, termin, capit, offic, either, calcul, yet, ill, consider, side, approv, request, shall, asset, product, feel, let, carol, someon, march, probabl, sent, commit, appropri, matter, depend, research, chris, keannaenronenron, quick, contain, hand, gari, respect, steve, guy, date, deal, tomorrow, repli, latest, interest, extens, scott, financi, liquid, texa, local, intern, entiti, vinc, anyth, team, read, great, via, risk, impact, proceed, desk, ive, done, harri, llc, afternoon, mari, michael, cours, best, perform, link, delay, account, committe, opportun, privileg, process, find, sure, mail, greg, send, legal, recipi, london, transact, confirm, mention, summari, convers, prior, visit, respond, bit, yesterday, websit, format, review, paper, georg, peter, handl, kelli, item, employe, net, provis, februari, suit, frank, robert, attend, strategi, dave, email.1, street, join, dan, list, ken, met, appreci, draft, contact, status, bob, version, type, appli, sorri, mark, credit, understand, kaminskihouect, etc, rick, brief, communic, document, bank, sheet, phone, gerald, ena, copi, fyi, counsel, john, updat, jeffrey, roger, lesli, lisa, agreement, volum, letter, elizabeth, .rnorm, settlement, discuss, houston, global, info, transfer, pleas, america, smith, stephani, memo, dear, regard, fax, form, counterparti, languag, mike, master, X77002, david, check, print, amend, eol, kate, attach, book, revis, thank
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 19.197 6.526
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8493426 0.9 0.7411168 0.5050335
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8893904 0.9358425 0.0226682 0.4558693
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0 0.2809365 0.1634241
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.1203919 0.2144132 0 4864.453
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.02422507 0.0567648# All X that is not user excluded
if (glb_is_classification && glb_is_binomial) {
model_id_pfx <- "Conditional.X"
# indep_vars_vctr <- setdiff(names(glb_trnent_df), union(glb_rsp_var, glb_exclude_vars_as_features))
indep_vars_vctr <- subset(glb_feats_df, is.ConditionalX.y &
(exclude.as.feat != 1))[, "id"]
} else {
model_id_pfx <- "All.X"
indep_vars_vctr <- subset(glb_feats_df,
(exclude.as.feat != 1))[, "id"]
}
for (method in glb_models_method_vctr) {
ret_lst <- myfit_mdl(model_id=paste0(model_id_pfx, ""), model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
# Since caret does not optimize rpart well
if (method == "rpart")
ret_lst <- myfit_mdl(model_id=paste0(model_id_pfx, ".cp.0"), model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df,
n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
# Compare how rf performs w/i & w/o .rnorm
if (method == "rf")
ret_lst <- myfit_mdl(model_id=paste0(model_id_pfx, ".no.rnorm"), model_method=method,
indep_vars_vctr=setdiff(indep_vars_vctr, c(".rnorm")),
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
}## [1] "fitting model: Conditional.X.glm"
## [1] " indep_vars: price, independ, demand, generat, suppli, emerg, summer, ferc, southern, util, custom, dow, high, oper, california, transmiss, natur, power, iso, jone, forc, gas, longer, capac, low, increas, expect, problem, consum, pay, system, plant, take, higher, rate, feder, said, continu, cap, commiss, sourc, solut, three, among, right, addit, order, author, line, energi, bring, push, major, load, told, can, lower, public, pipelin, peak, cost, supplier, without, electr, state, copyright, hour, interconnect, econom, two, implement, analyst, june, million, level, close, establish, want, within, purchas, other, part, announc, enough, servic, market, inc, grid, provid, competit, reduc, regulatori, news, steffesnaenronenron, creat, propos, still, situat, profit, X2000, whether, wholesal, transport, paid, fall, action, serv, rais, fuel, technolog, avail, will, today, full, unit, face, pass, compani, requir, cut, point, step, even, andor, earlier, design, past, result, pacif, sell, mani, reason, also, reserv, one, nation, time, consid, billion, percent, deregul, buy, industri, money, key, presid, day, set, remain, issu, success, plan, edison, call, accord, last, sever, own, earli, spot, includ, year, appear, bid, base, balanc, get, staff, anoth, deliveri, sinc, got, control, allow, now, resourc, jeff, small, articl, charg, per, washington, dollar, better, seller, contract, benefit, board, larg, wednesday, help, near, sold, term, turn, construct, five, much, like, around, amount, direct, period, top, structur, offici, governor, signific, meet, rather, end, friday, report, may, fact, dont, agenc, offer, use, follow, file, taken, purpos, opinion, talk, certain, grow, good, keep, altern, busi, dasovichnaenron, exist, come, make, real, drive, avoid, pge, monday, effort, effici, month, back, repres, measur, davi, determin, exchang, develop, lead, need, januari, payment, caus, immedi, least, run, explain, messag, havent, next., tuesday, occur, san, receiv, program, upon, start, refer, ago, prohibit, approxim, although, differ, releas, legisl, analysi, just, later, open, chairman, region, move, clear, administr, forward, post, director, decemb, produc, believ, estim, alreadi, fix, found, recent, tri, consult, facil, particip, trader, seek, see, novemb, crisi, cpuc, richard, far, entir, investig, polici, retail, indic, free, execut, respons, materi, modifi, X100, citi, X2001, steven, new, third, cover, oblig, long, portion, august, suggest, basi, idea, return, possibl, tariff, procedur, might, person, regul, readi, condit, agre, though, due, four, combin, X1999, big, someth, comput, look, averag, replac, everi, member, press, tom, record, applic, focus, way, hold, particular, short, decis, becom, jame, connect, corp, posit, thing, coupl, howev, court, manag, potenti, littl, juli, thursday, made, put, detail, begin, answer, site, well, must, total, excess, effect, enter, involv, complet, chanc, togeth, negoti, therefor, advanc, notic, ensur, brian, except, home, morn, give, act, storag, accept, coordin, unless, confer, otherwis, privat, futur, publish, share, fail, web, inform, given, arrang, that, statement, project, soon, current, second, question, first, week, william, ask, commod, say, hope, schedul, intend, jim, reach, less, case, outsid, daili, section, instead, name, place, rule, guarante, address, govern, associ, practic, subject, lot, access, trade, locat, susan, model, invest, main, build, depart, financ, note, left, identifi, internet, reflect, actual, express, bill, general, support, import, qualiti, confidenti, view, show, comment, peopl, eric, correct, work, similar, assum, fund, dasovich, late, sign, seem, seen, cash, option, tell, add, word, commerci, flow, sale, limit, attorney, previous, concern, submit, karen, approach, data, necessari, decid, various, recommend, abl, along, water, physic, claim, final, error, number, event, think, paul, alan, april, parti, exampl, els, group, anyon, dissemin, distribut, onlin, realli, south, delet, improv, sue, law, individu, oil, prepar, hear, leav, advis, head, standard, cynthia, joe, notifi, speak, thought, jan, tim, know, awar, strong, page, mean, sender, activ, organ, east, present, protect, corpor, west, kevin, enron, initi, singl, area, relat, kind, everyon, firm, electron, extend, assist, affect, octob, origin, specif, attent, septemb, deliv, chang, york, rob, hard, north, valu, termin, capit, offic, either, calcul, yet, ill, consider, side, approv, request, shall, asset, product, feel, let, carol, someon, march, probabl, sent, commit, appropri, matter, depend, research, chris, keannaenronenron, quick, contain, hand, gari, respect, steve, guy, date, deal, tomorrow, repli, latest, interest, extens, scott, financi, liquid, texa, local, intern, entiti, vinc, anyth, team, read, great, via, risk, impact, proceed, desk, ive, done, harri, llc, afternoon, mari, michael, cours, best, perform, link, delay, account, committe, opportun, privileg, process, find, sure, mail, greg, send, legal, recipi, london, transact, confirm, mention, summari, convers, prior, visit, respond, bit, yesterday, websit, format, review, paper, georg, peter, handl, kelli, item, employe, net, provis, februari, suit, frank, robert, attend, strategi, dave, email.1, street, join, dan, list, ken, met, appreci, draft, contact, status, bob, version, type, appli, sorri, mark, credit, understand, kaminskihouect, etc, rick, brief, communic, document, bank, sheet, phone, gerald, ena, copi, fyi, counsel, john, updat, jeffrey, roger, lesli, lisa, agreement, volum, letter, elizabeth, .rnorm, settlement, discuss, houston, global, info, transfer, pleas, america, europ, smith, stephani, memo, dear, regard, fax, form, counterparti, languag, mike, master, X77002, david, check, print, amend, eol, kate, attach, book, X1400, revis, thank"
## + Fold1: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold1: parameter=none
## + Fold2: parameter=none## Warning: glm.fit: algorithm did not converge## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold2: parameter=none
## + Fold3: parameter=none## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## - Fold3: parameter=none
## Aggregating results
## Fitting final model on full training set## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred## Warning: not plotting observations with leverage one:
## 1, 4, 7, 9, 10, 11, 12, 13, 14, 15, 17, 19, 21, 22, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39, 41, 42, 43, 44, 46, 49, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 68, 69, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110, 111, 113, 116, 117, 118, 119, 120, 121, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 387, 388, 389, 390, 391, 392, 393, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 417, 418, 419, 421, 422, 423, 425, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598
## Warning: not plotting observations with leverage one:
## 1, 4, 7, 9, 10, 11, 12, 13, 14, 15, 17, 19, 21, 22, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39, 41, 42, 43, 44, 46, 49, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 68, 69, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110, 111, 113, 116, 117, 118, 119, 120, 121, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 387, 388, 389, 390, 391, 392, 393, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 417, 418, 419, 421, 422, 423, 425, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579, 580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595, 596, 597, 598
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced##
## Call:
## NULL
##
## Deviance Residuals:
## 1 2 3 4 5 6 8 11 12 16
## 0.00 0.00 -8.49 0.00 -8.49 -8.49 -8.49 -8.49 0.00 0.00
## 18 19 21 23 24 25 28 30 31 34
## 0.00 0.00 0.00 0.00 0.00 -8.49 0.00 0.00 0.00 -8.49
## 37 39 41 42 43 45 46 49 50 51
## 0.00 -8.49 -8.49 -8.49 0.00 0.00 8.49 0.00 0.00 -8.49
## 52 53 54 55 56 57 58 59 60 61
## -8.49 0.00 -8.49 0.00 -8.49 0.00 0.00 0.00 0.00 -8.49
## 62 63 64 65 66 67 68 70 73 74
## 0.00 0.00 0.00 0.00 -8.49 -8.49 0.00 0.00 0.00 0.00
## 75 77 78 79 80 82 83 85 86 87
## 0.00 0.00 0.00 8.49 -8.49 0.00 0.00 -8.49 0.00 0.00
## 88 89 90 93 94 95 96 97 98 100
## 0.00 0.00 -8.49 0.00 -8.49 0.00 0.00 -8.49 0.00 -8.49
## 101 103 104 105 107 108 113 117 118 119
## 0.00 0.00 0.00 0.00 -8.49 0.00 0.00 0.00 8.49 -8.49
## 121 122 123 124 125 126 127 130 133 135
## 0.00 0.00 0.00 8.49 -8.49 -8.49 0.00 -8.49 0.00 -8.49
## 137 138 139 140 141 142 143 144 145 146
## -8.49 -8.49 -8.49 -8.49 0.00 0.00 0.00 0.00 -8.49 8.49
## 147 148 149 150 151 154 155 156 157 159
## 0.00 0.00 0.00 -8.49 8.49 0.00 0.00 0.00 0.00 0.00
## 160 162 165 166 167 168 169 171 172 173
## -8.49 0.00 0.00 0.00 8.49 0.00 0.00 0.00 -8.49 0.00
## 175 176 178 179 181 182 183 184 185 186
## 0.00 0.00 -8.49 8.49 0.00 0.00 0.00 -8.49 0.00 0.00
## 187 188 190 191 192 194 196 197 198 199
## -8.49 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 200 203 205 206 207 210 211 212 213 214
## 0.00 -8.49 8.49 0.00 0.00 -8.49 0.00 0.00 0.00 0.00
## 215 216 217 218 221 223 224 225 226 228
## 0.00 0.00 0.00 0.00 0.00 -8.49 0.00 0.00 0.00 0.00
## 229 231 232 234 235 237 238 239 240 242
## -8.49 0.00 0.00 0.00 8.49 0.00 0.00 0.00 0.00 0.00
## 244 245 246 247 250 251 252 254 256 257
## 0.00 0.00 -8.49 -8.49 0.00 0.00 0.00 0.00 0.00 0.00
## 258 261 262 263 264 265 267 268 271 272
## 8.49 0.00 0.00 0.00 0.00 -8.49 0.00 0.00 0.00 -8.49
## 273 274 277 278 279 281 283 284 285 287
## -8.49 0.00 0.00 -8.49 0.00 0.00 -8.49 -8.49 0.00 8.49
## 290 291 292 293 294 295 298 302 305 306
## 0.00 8.49 0.00 0.00 -8.49 0.00 0.00 0.00 0.00 0.00
## 307 308 309 310 311 312 313 316 317 318
## 0.00 0.00 0.00 0.00 0.00 0.00 -8.49 0.00 0.00 0.00
## 322 323 324 325 326 327 328 329 331 332
## 0.00 0.00 0.00 0.00 -8.49 0.00 0.00 0.00 8.49 0.00
## 333 334 335 338 339 340 341 342 343 345
## 0.00 0.00 8.49 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 346 348 349 350 352 353 354 357 358 360
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 8.49 0.00 0.00
## 361 363 364 365 367 368 369 370 372 373
## 0.00 -8.49 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 374 375 377 378 379 380 381 382 383 384
## 0.00 0.00 8.49 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 385 386 387 388 390 391 392 394 395 396
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 398 399 400 401 402 404 405 406 409 411
## 0.00 8.49 0.00 0.00 -8.49 0.00 0.00 0.00 8.49 0.00
## 414 417 418 420 421 422 423 424 426 428
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 429 430 432 433 434 438 439 440 441 443
## 0.00 -8.49 0.00 0.00 -8.49 0.00 0.00 0.00 0.00 0.00
## 444 446 447 448 449 450 451 453 454 455
## 0.00 0.00 8.49 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 457 458 459 460 461 462 463 465 467 468
## -8.49 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -8.49
## 469 471 474 477 478 479 480 482 483 484
## 0.00 0.00 0.00 -8.49 0.00 0.00 0.00 0.00 -8.49 0.00
## 485 488 489 490 491 499 501 502 503 504
## 0.00 0.00 0.00 -8.49 0.00 0.00 0.00 0.00 0.00 0.00
## 505 509 510 512 513 514 516 517 518 519
## -8.49 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 520 521 523 524 525 526 527 528 529 530
## 0.00 0.00 -8.49 0.00 0.00 -8.49 -8.49 0.00 0.00 0.00
## 531 533 536 537 538 539 540 541 542 543
## -8.49 0.00 0.00 0.00 8.49 0.00 -8.49 0.00 0.00 0.00
## 545 546 548 549 550 551 552 553 555 556
## 0.00 0.00 -8.49 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 557 558 560 561 563 564 565 566 567 569
## 0.00 0.00 0.00 0.00 -8.49 0.00 0.00 0.00 0.00 0.00
## 570 571 573 575 576 577 578 580 581 582
## 0.00 0.00 0.00 -8.49 0.00 0.00 0.00 0.00 0.00 -8.49
## 583 584 586 587 588 590 593 594 595 596
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 597 598 599 600 601 602 603 604 605 607
## 0.00 0.00 0.00 0.00 -8.49 0.00 0.00 0.00 0.00 0.00
## 608 609 612 614 616 618 619 620 621 622
## 0.00 0.00 -8.49 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 624 627 628 629 630 631 632 633 634 636
## 0.00 -8.49 -8.49 0.00 8.49 0.00 0.00 0.00 0.00 0.00
## 637 639 640 642 643 644 645 646 647 648
## 0.00 0.00 0.00 0.00 0.00 -8.49 0.00 0.00 0.00 0.00
## 649 650 652 653 654 655 656 657 658 660
## 0.00 0.00 -8.49 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 662 663 665 666 667 668 669 671 672 673
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 674 675 677 678 679 683 684 685 686 687
## -8.49 0.00 0.00 -8.49 0.00 0.00 -8.49 0.00 0.00 0.00
## 693 694 695 696 698 700 701 704 705 706
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 707 709 710 711 713 714 715 716 717 718
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 719 720 721 723 726 728 730 733 735 736
## 0.00 0.00 8.49 0.00 -8.49 0.00 0.00 0.00 0.00 0.00
## 737 740 741 743 745 746 748 751 752 754
## -8.49 0.00 0.00 0.00 0.00 0.00 -8.49 -8.49 -8.49 0.00
## 755 759 761 763 764 765 766 767 768 770
## 0.00 -8.49 -8.49 0.00 0.00 -8.49 0.00 8.49 -8.49 -8.49
## 771 772 774 775 776 777 778 780 781 782
## 0.00 0.00 -8.49 0.00 0.00 -8.49 0.00 0.00 8.49 -8.49
## 783 784 785 786 789 790 791 793 794 797
## 0.00 -8.49 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
## 798 801 802 804 806 807 809 811 812 813
## 0.00 0.00 0.00 0.00 -8.49 -8.49 0.00 0.00 -8.49 0.00
## 814 819 820 821 822 825 828 830 831 832
## 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 8.49
## 834 835 836 838 839 840 841 842 844 845
## -8.49 0.00 0.00 0.00 0.00 0.00 0.00 8.49 0.00 0.00
## 847 848 849 850 851 852 853 855
## 0.00 0.00 0.00 0.00 0.00 -8.49 0.00 0.00
##
## Coefficients: (190 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.567e+16 5.522e+07 -645966909 <2e-16 ***
## price 3.352e+28 7.143e+20 46923626 <2e-16 ***
## independ 3.532e+28 7.527e+20 46923626 <2e-16 ***
## demand -2.578e+28 5.494e+20 -46923626 <2e-16 ***
## generat -2.700e+28 5.754e+20 -46923626 <2e-16 ***
## suppli 4.474e+28 9.535e+20 46923626 <2e-16 ***
## emerg -2.198e+29 4.683e+21 -46923626 <2e-16 ***
## summer -1.040e+29 2.216e+21 -46923626 <2e-16 ***
## ferc 5.550e+28 1.183e+21 46923626 <2e-16 ***
## southern -2.972e+29 6.333e+21 -46923626 <2e-16 ***
## util 7.430e+28 1.584e+21 46923626 <2e-16 ***
## custom -5.677e+28 1.210e+21 -46923626 <2e-16 ***
## dow 6.345e+28 1.352e+21 46923626 <2e-16 ***
## high 1.023e+29 2.180e+21 46923626 <2e-16 ***
## oper -2.605e+28 5.552e+20 -46923626 <2e-16 ***
## california -2.356e+28 5.021e+20 -46923626 <2e-16 ***
## transmiss -1.200e+29 2.557e+21 -46923626 <2e-16 ***
## natur -7.512e+28 1.601e+21 -46923626 <2e-16 ***
## power -5.732e+15 8.080e+07 -70943581 <2e-16 ***
## iso 9.140e+28 1.948e+21 46923626 <2e-16 ***
## jone -9.145e+28 1.949e+21 -46923626 <2e-16 ***
## forc 3.791e+28 8.080e+20 46923626 <2e-16 ***
## gas 3.397e+27 7.240e+19 46923626 <2e-16 ***
## longer 2.277e+29 4.853e+21 46923626 <2e-16 ***
## capac -2.917e+28 6.217e+20 -46923626 <2e-16 ***
## low 1.532e+29 3.264e+21 46923626 <2e-16 ***
## increas 1.240e+29 2.643e+21 46923626 <2e-16 ***
## expect 2.965e+28 6.319e+20 46923626 <2e-16 ***
## problem 2.486e+28 5.298e+20 46923626 <2e-16 ***
## consum -1.536e+29 3.273e+21 -46923626 <2e-16 ***
## pay 4.729e+27 1.008e+20 46923626 <2e-16 ***
## system 1.092e+29 2.327e+21 46923626 <2e-16 ***
## plant -1.344e+29 2.864e+21 -46923626 <2e-16 ***
## take 3.506e+28 7.472e+20 46923626 <2e-16 ***
## higher -8.020e+28 1.709e+21 -46923626 <2e-16 ***
## rate 1.090e+29 2.322e+21 46923626 <2e-16 ***
## feder -7.487e+27 1.596e+20 -46923626 <2e-16 ***
## said -7.646e+28 1.629e+21 -46923626 <2e-16 ***
## continu 2.722e+29 5.802e+21 46923626 <2e-16 ***
## cap -1.191e+29 2.538e+21 -46923626 <2e-16 ***
## commiss -1.652e+29 3.520e+21 -46923626 <2e-16 ***
## sourc -7.118e+28 1.517e+21 -46923626 <2e-16 ***
## solut -6.817e+27 1.453e+20 -46923626 <2e-16 ***
## three 2.432e+28 5.183e+20 46923626 <2e-16 ***
## among 3.721e+29 7.930e+21 46923626 <2e-16 ***
## right -6.627e+28 1.412e+21 -46923626 <2e-16 ***
## addit 4.147e+28 8.838e+20 46923626 <2e-16 ***
## order -4.664e+28 9.939e+20 -46923626 <2e-16 ***
## author 4.143e+27 8.829e+19 46923626 <2e-16 ***
## line 4.704e+28 1.002e+21 46923626 <2e-16 ***
## energi -4.350e+28 9.271e+20 -46923626 <2e-16 ***
## bring -1.186e+29 2.528e+21 -46923626 <2e-16 ***
## push 5.746e+29 1.224e+22 46923626 <2e-16 ***
## major 2.720e+29 5.796e+21 46923626 <2e-16 ***
## load 2.751e+28 5.863e+20 46923626 <2e-16 ***
## told 1.205e+29 2.568e+21 46923626 <2e-16 ***
## can -1.029e+27 2.193e+19 -46923626 <2e-16 ***
## lower -1.155e+29 2.462e+21 -46923626 <2e-16 ***
## public 1.143e+29 2.436e+21 46923626 <2e-16 ***
## pipelin -8.232e+28 1.754e+21 -46923626 <2e-16 ***
## peak 1.932e+28 4.116e+20 46923626 <2e-16 ***
## cost -4.428e+28 9.437e+20 -46923626 <2e-16 ***
## supplier 1.367e+29 2.914e+21 46923626 <2e-16 ***
## without -9.968e+28 2.124e+21 -46923626 <2e-16 ***
## electr 8.692e+28 1.852e+21 46923626 <2e-16 ***
## state -3.245e+28 6.916e+20 -46923626 <2e-16 ***
## copyright -1.957e+29 4.170e+21 -46923626 <2e-16 ***
## hour -4.325e+28 9.218e+20 -46923626 <2e-16 ***
## interconnect -5.277e+27 1.125e+20 -46923626 <2e-16 ***
## econom 2.919e+29 6.220e+21 46923626 <2e-16 ***
## two 7.541e+28 1.607e+21 46923626 <2e-16 ***
## implement -2.806e+29 5.981e+21 -46923626 <2e-16 ***
## analyst 8.674e+28 1.849e+21 46923626 <2e-16 ***
## june -1.941e+29 4.136e+21 -46923626 <2e-16 ***
## million -3.768e+15 7.382e+07 -51048572 <2e-16 ***
## level 1.414e+29 3.014e+21 46923626 <2e-16 ***
## close 6.416e+28 1.367e+21 46923626 <2e-16 ***
## establish -1.308e+29 2.788e+21 -46923626 <2e-16 ***
## want 8.226e+27 1.753e+20 46923626 <2e-16 ***
## within 2.179e+29 4.644e+21 46923626 <2e-16 ***
## purchas -5.969e+28 1.272e+21 -46923626 <2e-16 ***
## other -1.821e+29 3.881e+21 -46923626 <2e-16 ***
## part 7.957e+28 1.696e+21 46923626 <2e-16 ***
## announc -4.491e+29 9.571e+21 -46923626 <2e-16 ***
## enough 3.775e+28 8.044e+20 46923626 <2e-16 ***
## servic -2.866e+28 6.109e+20 -46923626 <2e-16 ***
## market 9.858e+27 2.101e+20 46923626 <2e-16 ***
## inc -7.282e+28 1.552e+21 -46923626 <2e-16 ***
## grid 1.262e+29 2.689e+21 46923626 <2e-16 ***
## provid 9.593e+28 2.044e+21 46923626 <2e-16 ***
## competit -1.821e+29 3.880e+21 -46923626 <2e-16 ***
## reduc 1.234e+29 2.631e+21 46923626 <2e-16 ***
## regulatori -2.826e+29 6.024e+21 -46923626 <2e-16 ***
## news 8.417e+28 1.794e+21 46923626 <2e-16 ***
## steffesnaenronenron 8.804e+28 1.876e+21 46923626 <2e-16 ***
## creat -4.807e+28 1.024e+21 -46923626 <2e-16 ***
## propos -2.848e+27 6.070e+19 -46923626 <2e-16 ***
## still -2.550e+28 5.434e+20 -46923626 <2e-16 ***
## situat -5.536e+28 1.180e+21 -46923626 <2e-16 ***
## profit -2.712e+29 5.780e+21 -46923626 <2e-16 ***
## X2000 -9.357e+28 1.994e+21 -46923626 <2e-16 ***
## whether -8.982e+28 1.914e+21 -46923626 <2e-16 ***
## wholesal -5.906e+28 1.259e+21 -46923626 <2e-16 ***
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## got -1.251e+29 2.665e+21 -46923626 <2e-16 ***
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## now -1.243e+29 2.650e+21 -46923626 <2e-16 ***
## resourc 3.952e+28 8.423e+20 46923626 <2e-16 ***
## jeff 8.175e+27 1.742e+20 46923626 <2e-16 ***
## small 1.506e+29 3.209e+21 46923626 <2e-16 ***
## articl 3.150e+28 6.713e+20 46923626 <2e-16 ***
## charg 1.985e+29 4.230e+21 46923626 <2e-16 ***
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## washington 1.275e+28 2.717e+20 46923626 <2e-16 ***
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## seller -4.374e+28 9.322e+20 -46923626 <2e-16 ***
## contract -6.633e+28 1.414e+21 -46923626 <2e-16 ***
## benefit 6.806e+29 1.451e+22 46923626 <2e-16 ***
## board -1.246e+29 2.655e+21 -46923626 <2e-16 ***
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## wednesday -6.734e+27 1.435e+20 -46923626 <2e-16 ***
## help -5.543e+26 1.181e+19 -46923626 <2e-16 ***
## near 1.559e+29 3.321e+21 46923626 <2e-16 ***
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## term 3.795e+27 8.088e+19 46923626 <2e-16 ***
## turn 1.226e+29 2.613e+21 46923626 <2e-16 ***
## construct -1.733e+29 3.692e+21 -46923626 <2e-16 ***
## five -3.993e+29 8.510e+21 -46923626 <2e-16 ***
## much -2.191e+28 4.669e+20 -46923626 <2e-16 ***
## like 2.804e+17 3.362e+08 833978157 <2e-16 ***
## around 3.673e+28 7.828e+20 46923626 <2e-16 ***
## amount -3.558e+28 7.583e+20 -46923626 <2e-16 ***
## direct -1.337e+29 2.848e+21 -46923626 <2e-16 ***
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## structur 1.283e+28 2.735e+20 46923626 <2e-16 ***
## offici -5.636e+29 1.201e+22 -46923626 <2e-16 ***
## governor 1.807e+28 3.852e+20 46923626 <2e-16 ***
## signific 3.569e+28 7.605e+20 46923626 <2e-16 ***
## meet 8.048e+28 1.715e+21 46923626 <2e-16 ***
## rather 3.638e+29 7.754e+21 46923626 <2e-16 ***
## end 9.796e+28 2.088e+21 46923626 <2e-16 ***
## friday -3.119e+27 6.647e+19 -46923626 <2e-16 ***
## report 4.154e+15 7.632e+07 54420968 <2e-16 ***
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## fact 1.594e+28 3.398e+20 46923626 <2e-16 ***
## dont 1.885e+27 4.018e+19 46923626 <2e-16 ***
## agenc 2.286e+29 4.872e+21 46923626 <2e-16 ***
## offer 4.090e+28 8.717e+20 46923626 <2e-16 ***
## use -6.734e+28 1.435e+21 -46923626 <2e-16 ***
## follow 4.717e+28 1.005e+21 46923626 <2e-16 ***
## file 3.471e+14 8.452e+07 4107358 <2e-16 ***
## taken -1.644e+29 3.504e+21 -46923626 <2e-16 ***
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## opinion 2.028e+29 4.323e+21 46923626 <2e-16 ***
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## certain -1.032e+29 2.200e+21 -46923626 <2e-16 ***
## grow 1.689e+29 3.600e+21 46923626 <2e-16 ***
## good 3.041e+28 6.481e+20 46923626 <2e-16 ***
## keep -1.574e+29 3.355e+21 -46923626 <2e-16 ***
## altern 7.240e+28 1.543e+21 46923626 <2e-16 ***
## busi 1.480e+29 3.154e+21 46923626 <2e-16 ***
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## exist -7.021e+28 1.496e+21 -46923626 <2e-16 ***
## come 2.542e+28 5.418e+20 46923626 <2e-16 ***
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## real 1.148e+29 2.447e+21 46923626 <2e-16 ***
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## avoid 6.729e+28 1.434e+21 46923626 <2e-16 ***
## pge -5.599e+28 1.193e+21 -46923626 <2e-16 ***
## monday 1.050e+29 2.237e+21 46923626 <2e-16 ***
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## effici -6.806e+28 1.451e+21 -46923626 <2e-16 ***
## month 1.309e+29 2.791e+21 46923626 <2e-16 ***
## back 8.401e+28 1.790e+21 46923626 <2e-16 ***
## repres 8.718e+28 1.858e+21 46923626 <2e-16 ***
## measur 3.029e+29 6.455e+21 46923626 <2e-16 ***
## davi 5.905e+28 1.258e+21 46923626 <2e-16 ***
## determin -8.256e+28 1.759e+21 -46923626 <2e-16 ***
## exchang 9.876e+28 2.105e+21 46923626 <2e-16 ***
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## need 8.226e+27 1.753e+20 46923626 <2e-16 ***
## januari -1.986e+28 4.233e+20 -46923626 <2e-16 ***
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## immedi -9.106e+28 1.941e+21 -46923626 <2e-16 ***
## least 2.711e+29 5.778e+21 46923626 <2e-16 ***
## run -2.729e+29 5.817e+21 -46923626 <2e-16 ***
## explain 1.446e+29 3.081e+21 46923626 <2e-16 ***
## messag -8.485e+16 1.921e+08 -441594666 <2e-16 ***
## havent 1.352e+28 2.881e+20 46923626 <2e-16 ***
## next. 2.908e+28 6.197e+20 46923626 <2e-16 ***
## tuesday -4.346e+28 9.261e+20 -46923626 <2e-16 ***
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## receiv -7.239e+28 1.543e+21 -46923626 <2e-16 ***
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## analysi 2.152e+28 4.586e+20 46923626 <2e-16 ***
## just 3.796e+28 8.090e+20 46923626 <2e-16 ***
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## open 1.841e+29 3.924e+21 46923626 <2e-16 ***
## chairman 2.904e+28 6.190e+20 46923626 <2e-16 ***
## region -4.192e+28 8.935e+20 -46923626 <2e-16 ***
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## decemb 8.357e+28 1.781e+21 46923626 <2e-16 ***
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## believ -1.828e+29 3.895e+21 -46923626 <2e-16 ***
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## alreadi 1.287e+29 2.744e+21 46923626 <2e-16 ***
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## found 8.639e+28 1.841e+21 46923626 <2e-16 ***
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## particip 4.571e+28 9.742e+20 46923626 <2e-16 ***
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## see 2.860e+15 7.449e+07 38389661 <2e-16 ***
## novemb 4.570e+28 9.739e+20 46923626 <2e-16 ***
## crisi 1.182e+28 2.520e+20 46923626 <2e-16 ***
## cpuc 2.255e+28 4.806e+20 46923626 <2e-16 ***
## richard -2.431e+28 5.180e+20 -46923626 <2e-16 ***
## far 4.303e+28 9.169e+20 46923626 <2e-16 ***
## entir 6.792e+28 1.448e+21 46923626 <2e-16 ***
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## retail 7.506e+28 1.600e+21 46923626 <2e-16 ***
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## respons 3.224e+15 7.468e+07 43162883 <2e-16 ***
## materi -5.694e+27 1.213e+20 -46923626 <2e-16 ***
## modifi -1.516e+28 3.232e+20 -46923626 <2e-16 ***
## X100 7.426e+28 1.583e+21 46923626 <2e-16 ***
## citi -2.928e+29 6.241e+21 -46923626 <2e-16 ***
## X2001 -3.838e+28 8.180e+20 -46923626 <2e-16 ***
## steven -2.887e+28 6.153e+20 -46923626 <2e-16 ***
## new -1.941e+28 4.136e+20 -46923626 <2e-16 ***
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## cover -3.607e+29 7.687e+21 -46923626 <2e-16 ***
## oblig 2.463e+28 5.248e+20 46923626 <2e-16 ***
## long 2.803e+29 5.974e+21 46923626 <2e-16 ***
## portion -2.067e+29 4.404e+21 -46923626 <2e-16 ***
## august -7.087e+28 1.510e+21 -46923626 <2e-16 ***
## suggest -4.143e+28 8.830e+20 -46923626 <2e-16 ***
## basi 6.919e+28 1.475e+21 46923626 <2e-16 ***
## idea -2.060e+29 4.390e+21 -46923626 <2e-16 ***
## return -3.191e+28 6.800e+20 -46923626 <2e-16 ***
## possibl 2.884e+29 6.147e+21 46923626 <2e-16 ***
## tariff -2.539e+28 5.410e+20 -46923626 <2e-16 ***
## procedur 1.284e+29 2.736e+21 46923626 <2e-16 ***
## might -1.319e+29 2.811e+21 -46923626 <2e-16 ***
## person 4.001e+29 8.527e+21 46923626 <2e-16 ***
## regul 4.118e+28 8.775e+20 46923626 <2e-16 ***
## readi -1.875e+29 3.997e+21 -46923626 <2e-16 ***
## condit 1.295e+29 2.761e+21 46923626 <2e-16 ***
## agre -1.193e+29 2.542e+21 -46923626 <2e-16 ***
## though -3.076e+29 6.555e+21 -46923626 <2e-16 ***
## due -1.340e+29 2.856e+21 -46923626 <2e-16 ***
## four -6.497e+28 1.385e+21 -46923626 <2e-16 ***
## combin 2.053e+29 4.375e+21 46923626 <2e-16 ***
## X1999 -5.115e+29 1.090e+22 -46923626 <2e-16 ***
## big 5.879e+28 1.253e+21 46923626 <2e-16 ***
## someth 1.090e+29 2.322e+21 46923626 <2e-16 ***
## comput 1.699e+29 3.622e+21 46923626 <2e-16 ***
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## averag -8.861e+28 1.888e+21 -46923626 <2e-16 ***
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## everi -4.459e+29 9.503e+21 -46923626 <2e-16 ***
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## record 1.578e+28 3.362e+20 46923626 <2e-16 ***
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## focus -1.604e+29 3.419e+21 -46923626 <2e-16 ***
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## particular 2.730e+29 5.819e+21 46923626 <2e-16 ***
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## becom -1.282e+29 2.732e+21 -46923626 <2e-16 ***
## jame -2.428e+28 5.175e+20 -46923626 <2e-16 ***
## connect 9.229e+28 1.967e+21 46923626 <2e-16 ***
## corp -2.848e+27 6.070e+19 -46923626 <2e-16 ***
## posit -2.557e+28 5.449e+20 -46923626 <2e-16 ***
## thing 1.445e+29 3.079e+21 46923626 <2e-16 ***
## coupl 1.869e+29 3.984e+21 46923626 <2e-16 ***
## howev 6.083e+28 1.296e+21 46923626 <2e-16 ***
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## potenti -5.839e+28 1.244e+21 -46923626 <2e-16 ***
## littl -1.565e+28 3.335e+20 -46923626 <2e-16 ***
## juli 5.255e+28 1.120e+21 46923626 <2e-16 ***
## thursday 3.802e+27 8.102e+19 46923626 <2e-16 ***
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## begin 2.586e+29 5.511e+21 46923626 <2e-16 ***
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## excess 1.859e+29 3.961e+21 46923626 <2e-16 ***
## effect -6.739e+16 1.295e+08 -520413755 <2e-16 ***
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## involv -4.763e+28 1.015e+21 -46923626 <2e-16 ***
## complet -6.418e+28 1.368e+21 -46923626 <2e-16 ***
## chanc 3.094e+28 6.595e+20 46923626 <2e-16 ***
## togeth 3.046e+29 6.490e+21 46923626 <2e-16 ***
## negoti -3.522e+28 7.506e+20 -46923626 <2e-16 ***
## therefor 1.068e+29 2.275e+21 46923626 <2e-16 ***
## advanc -2.661e+28 5.672e+20 -46923626 <2e-16 ***
## notic -1.914e+16 1.225e+08 -156268648 <2e-16 ***
## ensur -1.469e+29 3.131e+21 -46923626 <2e-16 ***
## brian 8.175e+27 1.742e+20 46923626 <2e-16 ***
## except -2.427e+28 5.172e+20 -46923626 <2e-16 ***
## home -1.245e+29 2.653e+21 -46923626 <2e-16 ***
## morn 9.243e+28 1.970e+21 46923626 <2e-16 ***
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## .rnorm -5.699e+14 2.528e+07 -22539362 <2e-16 ***
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## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 530.2 on 597 degrees of freedom
## Residual deviance: 9299.3 on 4 degrees of freedom
## AIC: 10487
##
## Number of Fisher Scoring iterations: 16## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Reference
## Prediction N Y
## N 0 0
## Y 501 97
## responsive.fctr responsive.fctr.predict.Conditional.X.glm.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Conditional.X.glm.Y
## 1 501
## 2 97
## Reference
## Prediction N Y
## N 399 27
## Y 102 70
## responsive.fctr responsive.fctr.predict.Conditional.X.glm.N
## 1 N 399
## 2 Y 27
## responsive.fctr.predict.Conditional.X.glm.Y
## 1 102
## 2 70
## Reference
## Prediction N Y
## N 399 27
## Y 102 70
## responsive.fctr responsive.fctr.predict.Conditional.X.glm.N
## 1 N 399
## 2 Y 27
## responsive.fctr.predict.Conditional.X.glm.Y
## 1 102
## 2 70
## Reference
## Prediction N Y
## N 399 27
## Y 102 70
## responsive.fctr responsive.fctr.predict.Conditional.X.glm.N
## 1 N 399
## 2 Y 27
## responsive.fctr.predict.Conditional.X.glm.Y
## 1 102
## 2 70
## Reference
## Prediction N Y
## N 399 27
## Y 102 70
## responsive.fctr responsive.fctr.predict.Conditional.X.glm.N
## 1 N 399
## 2 Y 27
## responsive.fctr.predict.Conditional.X.glm.Y
## 1 102
## 2 70
## Reference
## Prediction N Y
## N 399 27
## Y 102 70
## responsive.fctr responsive.fctr.predict.Conditional.X.glm.N
## 1 N 399
## 2 Y 27
## responsive.fctr.predict.Conditional.X.glm.Y
## 1 102
## 2 70
## Reference
## Prediction N Y
## N 399 27
## Y 102 70
## responsive.fctr responsive.fctr.predict.Conditional.X.glm.N
## 1 N 399
## 2 Y 27
## responsive.fctr.predict.Conditional.X.glm.Y
## 1 102
## 2 70
## Reference
## Prediction N Y
## N 399 27
## Y 102 70
## responsive.fctr responsive.fctr.predict.Conditional.X.glm.N
## 1 N 399
## 2 Y 27
## responsive.fctr.predict.Conditional.X.glm.Y
## 1 102
## 2 70
## Reference
## Prediction N Y
## N 399 27
## Y 102 70
## responsive.fctr responsive.fctr.predict.Conditional.X.glm.N
## 1 N 399
## 2 Y 27
## responsive.fctr.predict.Conditional.X.glm.Y
## 1 102
## 2 70
## Reference
## Prediction N Y
## N 399 27
## Y 102 70
## responsive.fctr responsive.fctr.predict.Conditional.X.glm.N
## 1 N 399
## 2 Y 27
## responsive.fctr.predict.Conditional.X.glm.Y
## 1 102
## 2 70
## Reference
## Prediction N Y
## N 501 97
## Y 0 0
## responsive.fctr responsive.fctr.predict.Conditional.X.glm.N
## 1 N 501
## 2 Y 97
## responsive.fctr.predict.Conditional.X.glm.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2791367
## 2 0.1 0.5204461
## 3 0.2 0.5204461
## 4 0.3 0.5204461
## 5 0.4 0.5204461
## 6 0.5 0.5204461
## 7 0.6 0.5204461
## 8 0.7 0.5204461
## 9 0.8 0.5204461
## 10 0.9 0.5204461
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.9000 to maximize f.score.fit"
## responsive.fctr responsive.fctr.predict.Conditional.X.glm.N
## 1 N 399
## 2 Y 27
## responsive.fctr.predict.Conditional.X.glm.Y
## 1 102
## 2 70
## Reference
## Prediction N Y
## N 399 27
## Y 102 70
## responsive.fctr responsive.fctr.predict.Conditional.X.glm.N
## 1 N 399
## 2 Y 27
## responsive.fctr.predict.Conditional.X.glm.Y
## 1 102
## 2 70
## Prediction
## Reference N Y
## N 399 102
## Y 27 70
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 7.842809e-01 3.949362e-01 7.491135e-01 8.166122e-01 8.377926e-01
## AccuracyPValue McnemarPValue
## 9.997450e-01 7.252678e-11## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Reference
## Prediction N Y
## N 0 0
## Y 215 42
## responsive.fctr responsive.fctr.predict.Conditional.X.glm.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Conditional.X.glm.Y
## 1 215
## 2 42
## Reference
## Prediction N Y
## N 110 25
## Y 105 17
## responsive.fctr responsive.fctr.predict.Conditional.X.glm.N
## 1 N 110
## 2 Y 25
## responsive.fctr.predict.Conditional.X.glm.Y
## 1 105
## 2 17
## Reference
## Prediction N Y
## N 110 25
## Y 105 17
## responsive.fctr responsive.fctr.predict.Conditional.X.glm.N
## 1 N 110
## 2 Y 25
## responsive.fctr.predict.Conditional.X.glm.Y
## 1 105
## 2 17
## Reference
## Prediction N Y
## N 110 25
## Y 105 17
## responsive.fctr responsive.fctr.predict.Conditional.X.glm.N
## 1 N 110
## 2 Y 25
## responsive.fctr.predict.Conditional.X.glm.Y
## 1 105
## 2 17
## Reference
## Prediction N Y
## N 110 25
## Y 105 17
## responsive.fctr responsive.fctr.predict.Conditional.X.glm.N
## 1 N 110
## 2 Y 25
## responsive.fctr.predict.Conditional.X.glm.Y
## 1 105
## 2 17
## Reference
## Prediction N Y
## N 110 25
## Y 105 17
## responsive.fctr responsive.fctr.predict.Conditional.X.glm.N
## 1 N 110
## 2 Y 25
## responsive.fctr.predict.Conditional.X.glm.Y
## 1 105
## 2 17
## Reference
## Prediction N Y
## N 110 25
## Y 105 17
## responsive.fctr responsive.fctr.predict.Conditional.X.glm.N
## 1 N 110
## 2 Y 25
## responsive.fctr.predict.Conditional.X.glm.Y
## 1 105
## 2 17
## Reference
## Prediction N Y
## N 110 25
## Y 105 17
## responsive.fctr responsive.fctr.predict.Conditional.X.glm.N
## 1 N 110
## 2 Y 25
## responsive.fctr.predict.Conditional.X.glm.Y
## 1 105
## 2 17
## Reference
## Prediction N Y
## N 110 25
## Y 105 17
## responsive.fctr responsive.fctr.predict.Conditional.X.glm.N
## 1 N 110
## 2 Y 25
## responsive.fctr.predict.Conditional.X.glm.Y
## 1 105
## 2 17
## Reference
## Prediction N Y
## N 110 25
## Y 105 17
## responsive.fctr responsive.fctr.predict.Conditional.X.glm.N
## 1 N 110
## 2 Y 25
## responsive.fctr.predict.Conditional.X.glm.Y
## 1 105
## 2 17
## Reference
## Prediction N Y
## N 215 42
## Y 0 0
## responsive.fctr responsive.fctr.predict.Conditional.X.glm.N
## 1 N 215
## 2 Y 42
## responsive.fctr.predict.Conditional.X.glm.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2809365
## 2 0.1 0.2073171
## 3 0.2 0.2073171
## 4 0.3 0.2073171
## 5 0.4 0.2073171
## 6 0.5 0.2073171
## 7 0.6 0.2073171
## 8 0.7 0.2073171
## 9 0.8 0.2073171
## 10 0.9 0.2073171
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.0000 to maximize f.score.OOB"
## responsive.fctr responsive.fctr.predict.Conditional.X.glm.Y
## 1 N 215
## 2 Y 42
## Reference
## Prediction N Y
## N 0 0
## Y 215 42
## responsive.fctr responsive.fctr.predict.Conditional.X.glm.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Conditional.X.glm.Y
## 1 215
## 2 42
## Prediction
## Reference N Y
## N 0 215
## Y 0 42
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 1.634241e-01 0.000000e+00 1.203919e-01 2.144132e-01 8.365759e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 3.036391e-48
## model_id model_method
## 1 Conditional.X.glm glm
## feats
## 1 price, independ, demand, generat, suppli, emerg, summer, ferc, southern, util, custom, dow, high, oper, california, transmiss, natur, power, iso, jone, forc, gas, longer, capac, low, increas, expect, problem, consum, pay, system, plant, take, higher, rate, feder, said, continu, cap, commiss, sourc, solut, three, among, right, addit, order, author, line, energi, bring, push, major, load, told, can, lower, public, pipelin, peak, cost, supplier, without, electr, state, copyright, hour, interconnect, econom, two, implement, analyst, june, million, level, close, establish, want, within, purchas, other, part, announc, enough, servic, market, inc, grid, provid, competit, reduc, regulatori, news, steffesnaenronenron, creat, propos, still, situat, profit, X2000, whether, wholesal, transport, paid, fall, action, serv, rais, fuel, technolog, avail, will, today, full, unit, face, pass, compani, requir, cut, point, step, even, andor, earlier, design, past, result, pacif, sell, mani, reason, also, reserv, one, nation, time, consid, billion, percent, deregul, buy, industri, money, key, presid, day, set, remain, issu, success, plan, edison, call, accord, last, sever, own, earli, spot, includ, year, appear, bid, base, balanc, get, staff, anoth, deliveri, sinc, got, control, allow, now, resourc, jeff, small, articl, charg, per, washington, dollar, better, seller, contract, benefit, board, larg, wednesday, help, near, sold, term, turn, construct, five, much, like, around, amount, direct, period, top, structur, offici, governor, signific, meet, rather, end, friday, report, may, fact, dont, agenc, offer, use, follow, file, taken, purpos, opinion, talk, certain, grow, good, keep, altern, busi, dasovichnaenron, exist, come, make, real, drive, avoid, pge, monday, effort, effici, month, back, repres, measur, davi, determin, exchang, develop, lead, need, januari, payment, caus, immedi, least, run, explain, messag, havent, next., tuesday, occur, san, receiv, program, upon, start, refer, ago, prohibit, approxim, although, differ, releas, legisl, analysi, just, later, open, chairman, region, move, clear, administr, forward, post, director, decemb, produc, believ, estim, alreadi, fix, found, recent, tri, consult, facil, particip, trader, seek, see, novemb, crisi, cpuc, richard, far, entir, investig, polici, retail, indic, free, execut, respons, materi, modifi, X100, citi, X2001, steven, new, third, cover, oblig, long, portion, august, suggest, basi, idea, return, possibl, tariff, procedur, might, person, regul, readi, condit, agre, though, due, four, combin, X1999, big, someth, comput, look, averag, replac, everi, member, press, tom, record, applic, focus, way, hold, particular, short, decis, becom, jame, connect, corp, posit, thing, coupl, howev, court, manag, potenti, littl, juli, thursday, made, put, detail, begin, answer, site, well, must, total, excess, effect, enter, involv, complet, chanc, togeth, negoti, therefor, advanc, notic, ensur, brian, except, home, morn, give, act, storag, accept, coordin, unless, confer, otherwis, privat, futur, publish, share, fail, web, inform, given, arrang, that, statement, project, soon, current, second, question, first, week, william, ask, commod, say, hope, schedul, intend, jim, reach, less, case, outsid, daili, section, instead, name, place, rule, guarante, address, govern, associ, practic, subject, lot, access, trade, locat, susan, model, invest, main, build, depart, financ, note, left, identifi, internet, reflect, actual, express, bill, general, support, import, qualiti, confidenti, view, show, comment, peopl, eric, correct, work, similar, assum, fund, dasovich, late, sign, seem, seen, cash, option, tell, add, word, commerci, flow, sale, limit, attorney, previous, concern, submit, karen, approach, data, necessari, decid, various, recommend, abl, along, water, physic, claim, final, error, number, event, think, paul, alan, april, parti, exampl, els, group, anyon, dissemin, distribut, onlin, realli, south, delet, improv, sue, law, individu, oil, prepar, hear, leav, advis, head, standard, cynthia, joe, notifi, speak, thought, jan, tim, know, awar, strong, page, mean, sender, activ, organ, east, present, protect, corpor, west, kevin, enron, initi, singl, area, relat, kind, everyon, firm, electron, extend, assist, affect, octob, origin, specif, attent, septemb, deliv, chang, york, rob, hard, north, valu, termin, capit, offic, either, calcul, yet, ill, consider, side, approv, request, shall, asset, product, feel, let, carol, someon, march, probabl, sent, commit, appropri, matter, depend, research, chris, keannaenronenron, quick, contain, hand, gari, respect, steve, guy, date, deal, tomorrow, repli, latest, interest, extens, scott, financi, liquid, texa, local, intern, entiti, vinc, anyth, team, read, great, via, risk, impact, proceed, desk, ive, done, harri, llc, afternoon, mari, michael, cours, best, perform, link, delay, account, committe, opportun, privileg, process, find, sure, mail, greg, send, legal, recipi, london, transact, confirm, mention, summari, convers, prior, visit, respond, bit, yesterday, websit, format, review, paper, georg, peter, handl, kelli, item, employe, net, provis, februari, suit, frank, robert, attend, strategi, dave, email.1, street, join, dan, list, ken, met, appreci, draft, contact, status, bob, version, type, appli, sorri, mark, credit, understand, kaminskihouect, etc, rick, brief, communic, document, bank, sheet, phone, gerald, ena, copi, fyi, counsel, john, updat, jeffrey, roger, lesli, lisa, agreement, volum, letter, elizabeth, .rnorm, settlement, discuss, houston, global, info, transfer, pleas, america, europ, smith, stephani, memo, dear, regard, fax, form, counterparti, languag, mike, master, X77002, david, check, print, amend, eol, kate, attach, book, X1400, revis, thank
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 20.175 5.759
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.7590283 0.9 0.5204461 0.5452345
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.7491135 0.8166122 0.05021386 0.4581949
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0 0.2809365 0.1634241
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.1203919 0.2144132 0 10487.26
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.04357685 0.05204949
## [1] "fitting model: Conditional.X.rpart"
## [1] " indep_vars: price, independ, demand, generat, suppli, emerg, summer, ferc, southern, util, custom, dow, high, oper, california, transmiss, natur, power, iso, jone, forc, gas, longer, capac, low, increas, expect, problem, consum, pay, system, plant, take, higher, rate, feder, said, continu, cap, commiss, sourc, solut, three, among, right, addit, order, author, line, energi, bring, push, major, load, told, can, lower, public, pipelin, peak, cost, supplier, without, electr, state, copyright, hour, interconnect, econom, two, implement, analyst, june, million, level, close, establish, want, within, purchas, other, part, announc, enough, servic, market, inc, grid, provid, competit, reduc, regulatori, news, steffesnaenronenron, creat, propos, still, situat, profit, X2000, whether, wholesal, transport, paid, fall, action, serv, rais, fuel, technolog, avail, will, today, full, unit, face, pass, compani, requir, cut, point, step, even, andor, earlier, design, past, result, pacif, sell, mani, reason, also, reserv, one, nation, time, consid, billion, percent, deregul, buy, industri, money, key, presid, day, set, remain, issu, success, plan, edison, call, accord, last, sever, own, earli, spot, includ, year, appear, bid, base, balanc, get, staff, anoth, deliveri, sinc, got, control, allow, now, resourc, jeff, small, articl, charg, per, washington, dollar, better, seller, contract, benefit, board, larg, wednesday, help, near, sold, term, turn, construct, five, much, like, around, amount, direct, period, top, structur, offici, governor, signific, meet, rather, end, friday, report, may, fact, dont, agenc, offer, use, follow, file, taken, purpos, opinion, talk, certain, grow, good, keep, altern, busi, dasovichnaenron, exist, come, make, real, drive, avoid, pge, monday, effort, effici, month, back, repres, measur, davi, determin, exchang, develop, lead, need, januari, payment, caus, immedi, least, run, explain, messag, havent, next., tuesday, occur, san, receiv, program, upon, start, refer, ago, prohibit, approxim, although, differ, releas, legisl, analysi, just, later, open, chairman, region, move, clear, administr, forward, post, director, decemb, produc, believ, estim, alreadi, fix, found, recent, tri, consult, facil, particip, trader, seek, see, novemb, crisi, cpuc, richard, far, entir, investig, polici, retail, indic, free, execut, respons, materi, modifi, X100, citi, X2001, steven, new, third, cover, oblig, long, portion, august, suggest, basi, idea, return, possibl, tariff, procedur, might, person, regul, readi, condit, agre, though, due, four, combin, X1999, big, someth, comput, look, averag, replac, everi, member, press, tom, record, applic, focus, way, hold, particular, short, decis, becom, jame, connect, corp, posit, thing, coupl, howev, court, manag, potenti, littl, juli, thursday, made, put, detail, begin, answer, site, well, must, total, excess, effect, enter, involv, complet, chanc, togeth, negoti, therefor, advanc, notic, ensur, brian, except, home, morn, give, act, storag, accept, coordin, unless, confer, otherwis, privat, futur, publish, share, fail, web, inform, given, arrang, that, statement, project, soon, current, second, question, first, week, william, ask, commod, say, hope, schedul, intend, jim, reach, less, case, outsid, daili, section, instead, name, place, rule, guarante, address, govern, associ, practic, subject, lot, access, trade, locat, susan, model, invest, main, build, depart, financ, note, left, identifi, internet, reflect, actual, express, bill, general, support, import, qualiti, confidenti, view, show, comment, peopl, eric, correct, work, similar, assum, fund, dasovich, late, sign, seem, seen, cash, option, tell, add, word, commerci, flow, sale, limit, attorney, previous, concern, submit, karen, approach, data, necessari, decid, various, recommend, abl, along, water, physic, claim, final, error, number, event, think, paul, alan, april, parti, exampl, els, group, anyon, dissemin, distribut, onlin, realli, south, delet, improv, sue, law, individu, oil, prepar, hear, leav, advis, head, standard, cynthia, joe, notifi, speak, thought, jan, tim, know, awar, strong, page, mean, sender, activ, organ, east, present, protect, corpor, west, kevin, enron, initi, singl, area, relat, kind, everyon, firm, electron, extend, assist, affect, octob, origin, specif, attent, septemb, deliv, chang, york, rob, hard, north, valu, termin, capit, offic, either, calcul, yet, ill, consider, side, approv, request, shall, asset, product, feel, let, carol, someon, march, probabl, sent, commit, appropri, matter, depend, research, chris, keannaenronenron, quick, contain, hand, gari, respect, steve, guy, date, deal, tomorrow, repli, latest, interest, extens, scott, financi, liquid, texa, local, intern, entiti, vinc, anyth, team, read, great, via, risk, impact, proceed, desk, ive, done, harri, llc, afternoon, mari, michael, cours, best, perform, link, delay, account, committe, opportun, privileg, process, find, sure, mail, greg, send, legal, recipi, london, transact, confirm, mention, summari, convers, prior, visit, respond, bit, yesterday, websit, format, review, paper, georg, peter, handl, kelli, item, employe, net, provis, februari, suit, frank, robert, attend, strategi, dave, email.1, street, join, dan, list, ken, met, appreci, draft, contact, status, bob, version, type, appli, sorri, mark, credit, understand, kaminskihouect, etc, rick, brief, communic, document, bank, sheet, phone, gerald, ena, copi, fyi, counsel, john, updat, jeffrey, roger, lesli, lisa, agreement, volum, letter, elizabeth, settlement, discuss, houston, global, info, transfer, pleas, america, europ, smith, stephani, memo, dear, regard, fax, form, counterparti, languag, mike, master, X77002, david, check, print, amend, eol, kate, attach, book, X1400, revis, thank"
## + Fold1: cp=0.02062
## - Fold1: cp=0.02062
## + Fold2: cp=0.02062
## - Fold2: cp=0.02062
## + Fold3: cp=0.02062
## - Fold3: cp=0.02062
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0619 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 598
##
## CP nsplit rel error
## 1 0.29896907 0 1.0000000
## 2 0.06185567 1 0.7010309
##
## Variable importance
## california wholesal edison said southern util
## 39 14 12 12 12 11
##
## Node number 1: 598 observations, complexity param=0.2989691
## predicted class=N expected loss=0.1622074 P(node) =1
## class counts: 501 97
## probabilities: 0.838 0.162
## left son=2 (547 obs) right son=3 (51 obs)
## Primary splits:
## california < 1.5 to the left, improve=43.15621, (0 missing)
## price < 1.5 to the left, improve=38.19520, (0 missing)
## electr < 1.5 to the left, improve=36.87857, (0 missing)
## state < 1.5 to the left, improve=33.95206, (0 missing)
## demand < 0.5 to the left, improve=31.57326, (0 missing)
## Surrogate splits:
## wholesal < 0.5 to the left, agree=0.945, adj=0.353, (0 split)
## southern < 0.5 to the left, agree=0.941, adj=0.314, (0 split)
## said < 2.5 to the left, agree=0.941, adj=0.314, (0 split)
## edison < 0.5 to the left, agree=0.941, adj=0.314, (0 split)
## util < 2.5 to the left, agree=0.940, adj=0.294, (0 split)
##
## Node number 2: 547 observations
## predicted class=N expected loss=0.1042048 P(node) =0.9147157
## class counts: 490 57
## probabilities: 0.896 0.104
##
## Node number 3: 51 observations
## predicted class=Y expected loss=0.2156863 P(node) =0.08528428
## class counts: 11 40
## probabilities: 0.216 0.784
##
## n= 598
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 598 97 N (0.8377926 0.1622074)
## 2) california< 1.5 547 57 N (0.8957952 0.1042048) *
## 3) california>=1.5 51 11 Y (0.2156863 0.7843137) *
## Reference
## Prediction N Y
## N 0 0
## Y 501 97
## responsive.fctr responsive.fctr.predict.Conditional.X.rpart.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Conditional.X.rpart.Y
## 1 501
## 2 97
## Reference
## Prediction N Y
## N 0 0
## Y 501 97
## responsive.fctr responsive.fctr.predict.Conditional.X.rpart.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Conditional.X.rpart.Y
## 1 501
## 2 97
## Reference
## Prediction N Y
## N 490 57
## Y 11 40
## responsive.fctr responsive.fctr.predict.Conditional.X.rpart.N
## 1 N 490
## 2 Y 57
## responsive.fctr.predict.Conditional.X.rpart.Y
## 1 11
## 2 40
## Reference
## Prediction N Y
## N 490 57
## Y 11 40
## responsive.fctr responsive.fctr.predict.Conditional.X.rpart.N
## 1 N 490
## 2 Y 57
## responsive.fctr.predict.Conditional.X.rpart.Y
## 1 11
## 2 40
## Reference
## Prediction N Y
## N 490 57
## Y 11 40
## responsive.fctr responsive.fctr.predict.Conditional.X.rpart.N
## 1 N 490
## 2 Y 57
## responsive.fctr.predict.Conditional.X.rpart.Y
## 1 11
## 2 40
## Reference
## Prediction N Y
## N 490 57
## Y 11 40
## responsive.fctr responsive.fctr.predict.Conditional.X.rpart.N
## 1 N 490
## 2 Y 57
## responsive.fctr.predict.Conditional.X.rpart.Y
## 1 11
## 2 40
## Reference
## Prediction N Y
## N 490 57
## Y 11 40
## responsive.fctr responsive.fctr.predict.Conditional.X.rpart.N
## 1 N 490
## 2 Y 57
## responsive.fctr.predict.Conditional.X.rpart.Y
## 1 11
## 2 40
## Reference
## Prediction N Y
## N 490 57
## Y 11 40
## responsive.fctr responsive.fctr.predict.Conditional.X.rpart.N
## 1 N 490
## 2 Y 57
## responsive.fctr.predict.Conditional.X.rpart.Y
## 1 11
## 2 40
## Reference
## Prediction N Y
## N 501 97
## Y 0 0
## responsive.fctr responsive.fctr.predict.Conditional.X.rpart.N
## 1 N 501
## 2 Y 97
## responsive.fctr.predict.Conditional.X.rpart.Y
## 1 0
## 2 0
## Reference
## Prediction N Y
## N 501 97
## Y 0 0
## responsive.fctr responsive.fctr.predict.Conditional.X.rpart.N
## 1 N 501
## 2 Y 97
## responsive.fctr.predict.Conditional.X.rpart.Y
## 1 0
## 2 0
## Reference
## Prediction N Y
## N 501 97
## Y 0 0
## responsive.fctr responsive.fctr.predict.Conditional.X.rpart.N
## 1 N 501
## 2 Y 97
## responsive.fctr.predict.Conditional.X.rpart.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2791367
## 2 0.1 0.2791367
## 3 0.2 0.5405405
## 4 0.3 0.5405405
## 5 0.4 0.5405405
## 6 0.5 0.5405405
## 7 0.6 0.5405405
## 8 0.7 0.5405405
## 9 0.8 0.0000000
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.7000 to maximize f.score.fit"
## responsive.fctr responsive.fctr.predict.Conditional.X.rpart.N
## 1 N 490
## 2 Y 57
## responsive.fctr.predict.Conditional.X.rpart.Y
## 1 11
## 2 40
## Reference
## Prediction N Y
## N 490 57
## Y 11 40
## responsive.fctr responsive.fctr.predict.Conditional.X.rpart.N
## 1 N 490
## 2 Y 57
## responsive.fctr.predict.Conditional.X.rpart.Y
## 1 11
## 2 40
## Prediction
## Reference N Y
## N 490 11
## Y 57 40
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.862876e-01 4.827121e-01 8.580773e-01 9.106084e-01 8.377926e-01
## AccuracyPValue McnemarPValue
## 4.959708e-04 4.841058e-08
## Reference
## Prediction N Y
## N 0 0
## Y 215 42
## responsive.fctr responsive.fctr.predict.Conditional.X.rpart.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Conditional.X.rpart.Y
## 1 215
## 2 42
## Reference
## Prediction N Y
## N 0 0
## Y 215 42
## responsive.fctr responsive.fctr.predict.Conditional.X.rpart.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Conditional.X.rpart.Y
## 1 215
## 2 42
## Reference
## Prediction N Y
## N 204 19
## Y 11 23
## responsive.fctr responsive.fctr.predict.Conditional.X.rpart.N
## 1 N 204
## 2 Y 19
## responsive.fctr.predict.Conditional.X.rpart.Y
## 1 11
## 2 23
## Reference
## Prediction N Y
## N 204 19
## Y 11 23
## responsive.fctr responsive.fctr.predict.Conditional.X.rpart.N
## 1 N 204
## 2 Y 19
## responsive.fctr.predict.Conditional.X.rpart.Y
## 1 11
## 2 23
## Reference
## Prediction N Y
## N 204 19
## Y 11 23
## responsive.fctr responsive.fctr.predict.Conditional.X.rpart.N
## 1 N 204
## 2 Y 19
## responsive.fctr.predict.Conditional.X.rpart.Y
## 1 11
## 2 23
## Reference
## Prediction N Y
## N 204 19
## Y 11 23
## responsive.fctr responsive.fctr.predict.Conditional.X.rpart.N
## 1 N 204
## 2 Y 19
## responsive.fctr.predict.Conditional.X.rpart.Y
## 1 11
## 2 23
## Reference
## Prediction N Y
## N 204 19
## Y 11 23
## responsive.fctr responsive.fctr.predict.Conditional.X.rpart.N
## 1 N 204
## 2 Y 19
## responsive.fctr.predict.Conditional.X.rpart.Y
## 1 11
## 2 23
## Reference
## Prediction N Y
## N 204 19
## Y 11 23
## responsive.fctr responsive.fctr.predict.Conditional.X.rpart.N
## 1 N 204
## 2 Y 19
## responsive.fctr.predict.Conditional.X.rpart.Y
## 1 11
## 2 23
## Reference
## Prediction N Y
## N 215 42
## Y 0 0
## responsive.fctr responsive.fctr.predict.Conditional.X.rpart.N
## 1 N 215
## 2 Y 42
## responsive.fctr.predict.Conditional.X.rpart.Y
## 1 0
## 2 0
## Reference
## Prediction N Y
## N 215 42
## Y 0 0
## responsive.fctr responsive.fctr.predict.Conditional.X.rpart.N
## 1 N 215
## 2 Y 42
## responsive.fctr.predict.Conditional.X.rpart.Y
## 1 0
## 2 0
## Reference
## Prediction N Y
## N 215 42
## Y 0 0
## responsive.fctr responsive.fctr.predict.Conditional.X.rpart.N
## 1 N 215
## 2 Y 42
## responsive.fctr.predict.Conditional.X.rpart.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2809365
## 2 0.1 0.2809365
## 3 0.2 0.6052632
## 4 0.3 0.6052632
## 5 0.4 0.6052632
## 6 0.5 0.6052632
## 7 0.6 0.6052632
## 8 0.7 0.6052632
## 9 0.8 0.0000000
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.7000 to maximize f.score.OOB"
## responsive.fctr responsive.fctr.predict.Conditional.X.rpart.N
## 1 N 204
## 2 Y 19
## responsive.fctr.predict.Conditional.X.rpart.Y
## 1 11
## 2 23
## Reference
## Prediction N Y
## N 204 19
## Y 11 23
## responsive.fctr responsive.fctr.predict.Conditional.X.rpart.N
## 1 N 204
## 2 Y 19
## responsive.fctr.predict.Conditional.X.rpart.Y
## 1 11
## 2 23
## Prediction
## Reference N Y
## N 204 11
## Y 19 23
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.88326848 0.53765891 0.83756707 0.91983996 0.83657588
## AccuracyPValue McnemarPValue
## 0.02267769 0.20124262
## model_id model_method
## 1 Conditional.X.rpart rpart
## feats
## 1 price, independ, demand, generat, suppli, emerg, summer, ferc, southern, util, custom, dow, high, oper, california, transmiss, natur, power, iso, jone, forc, gas, longer, capac, low, increas, expect, problem, consum, pay, system, plant, take, higher, rate, feder, said, continu, cap, commiss, sourc, solut, three, among, right, addit, order, author, line, energi, bring, push, major, load, told, can, lower, public, pipelin, peak, cost, supplier, without, electr, state, copyright, hour, interconnect, econom, two, implement, analyst, june, million, level, close, establish, want, within, purchas, other, part, announc, enough, servic, market, inc, grid, provid, competit, reduc, regulatori, news, steffesnaenronenron, creat, propos, still, situat, profit, X2000, whether, wholesal, transport, paid, fall, action, serv, rais, fuel, technolog, avail, will, today, full, unit, face, pass, compani, requir, cut, point, step, even, andor, earlier, design, past, result, pacif, sell, mani, reason, also, reserv, one, nation, time, consid, billion, percent, deregul, buy, industri, money, key, presid, day, set, remain, issu, success, plan, edison, call, accord, last, sever, own, earli, spot, includ, year, appear, bid, base, balanc, get, staff, anoth, deliveri, sinc, got, control, allow, now, resourc, jeff, small, articl, charg, per, washington, dollar, better, seller, contract, benefit, board, larg, wednesday, help, near, sold, term, turn, construct, five, much, like, around, amount, direct, period, top, structur, offici, governor, signific, meet, rather, end, friday, report, may, fact, dont, agenc, offer, use, follow, file, taken, purpos, opinion, talk, certain, grow, good, keep, altern, busi, dasovichnaenron, exist, come, make, real, drive, avoid, pge, monday, effort, effici, month, back, repres, measur, davi, determin, exchang, develop, lead, need, januari, payment, caus, immedi, least, run, explain, messag, havent, next., tuesday, occur, san, receiv, program, upon, start, refer, ago, prohibit, approxim, although, differ, releas, legisl, analysi, just, later, open, chairman, region, move, clear, administr, forward, post, director, decemb, produc, believ, estim, alreadi, fix, found, recent, tri, consult, facil, particip, trader, seek, see, novemb, crisi, cpuc, richard, far, entir, investig, polici, retail, indic, free, execut, respons, materi, modifi, X100, citi, X2001, steven, new, third, cover, oblig, long, portion, august, suggest, basi, idea, return, possibl, tariff, procedur, might, person, regul, readi, condit, agre, though, due, four, combin, X1999, big, someth, comput, look, averag, replac, everi, member, press, tom, record, applic, focus, way, hold, particular, short, decis, becom, jame, connect, corp, posit, thing, coupl, howev, court, manag, potenti, littl, juli, thursday, made, put, detail, begin, answer, site, well, must, total, excess, effect, enter, involv, complet, chanc, togeth, negoti, therefor, advanc, notic, ensur, brian, except, home, morn, give, act, storag, accept, coordin, unless, confer, otherwis, privat, futur, publish, share, fail, web, inform, given, arrang, that, statement, project, soon, current, second, question, first, week, william, ask, commod, say, hope, schedul, intend, jim, reach, less, case, outsid, daili, section, instead, name, place, rule, guarante, address, govern, associ, practic, subject, lot, access, trade, locat, susan, model, invest, main, build, depart, financ, note, left, identifi, internet, reflect, actual, express, bill, general, support, import, qualiti, confidenti, view, show, comment, peopl, eric, correct, work, similar, assum, fund, dasovich, late, sign, seem, seen, cash, option, tell, add, word, commerci, flow, sale, limit, attorney, previous, concern, submit, karen, approach, data, necessari, decid, various, recommend, abl, along, water, physic, claim, final, error, number, event, think, paul, alan, april, parti, exampl, els, group, anyon, dissemin, distribut, onlin, realli, south, delet, improv, sue, law, individu, oil, prepar, hear, leav, advis, head, standard, cynthia, joe, notifi, speak, thought, jan, tim, know, awar, strong, page, mean, sender, activ, organ, east, present, protect, corpor, west, kevin, enron, initi, singl, area, relat, kind, everyon, firm, electron, extend, assist, affect, octob, origin, specif, attent, septemb, deliv, chang, york, rob, hard, north, valu, termin, capit, offic, either, calcul, yet, ill, consider, side, approv, request, shall, asset, product, feel, let, carol, someon, march, probabl, sent, commit, appropri, matter, depend, research, chris, keannaenronenron, quick, contain, hand, gari, respect, steve, guy, date, deal, tomorrow, repli, latest, interest, extens, scott, financi, liquid, texa, local, intern, entiti, vinc, anyth, team, read, great, via, risk, impact, proceed, desk, ive, done, harri, llc, afternoon, mari, michael, cours, best, perform, link, delay, account, committe, opportun, privileg, process, find, sure, mail, greg, send, legal, recipi, london, transact, confirm, mention, summari, convers, prior, visit, respond, bit, yesterday, websit, format, review, paper, georg, peter, handl, kelli, item, employe, net, provis, februari, suit, frank, robert, attend, strategi, dave, email.1, street, join, dan, list, ken, met, appreci, draft, contact, status, bob, version, type, appli, sorri, mark, credit, understand, kaminskihouect, etc, rick, brief, communic, document, bank, sheet, phone, gerald, ena, copi, fyi, counsel, john, updat, jeffrey, roger, lesli, lisa, agreement, volum, letter, elizabeth, settlement, discuss, houston, global, info, transfer, pleas, america, europ, smith, stephani, memo, dear, regard, fax, form, counterparti, languag, mike, master, X77002, david, check, print, amend, eol, kate, attach, book, X1400, revis, thank
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 7.65 1.136
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.6952075 0.7 0.5405405 0.8812563
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8580773 0.9106084 0.4641375 0.7482281
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.7 0.6052632 0.8832685
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8375671 0.91984 0.5376589
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.02384723 0.1330873
## [1] "fitting model: Conditional.X.cp.0.rpart"
## [1] " indep_vars: price, independ, demand, generat, suppli, emerg, summer, ferc, southern, util, custom, dow, high, oper, california, transmiss, natur, power, iso, jone, forc, gas, longer, capac, low, increas, expect, problem, consum, pay, system, plant, take, higher, rate, feder, said, continu, cap, commiss, sourc, solut, three, among, right, addit, order, author, line, energi, bring, push, major, load, told, can, lower, public, pipelin, peak, cost, supplier, without, electr, state, copyright, hour, interconnect, econom, two, implement, analyst, june, million, level, close, establish, want, within, purchas, other, part, announc, enough, servic, market, inc, grid, provid, competit, reduc, regulatori, news, steffesnaenronenron, creat, propos, still, situat, profit, X2000, whether, wholesal, transport, paid, fall, action, serv, rais, fuel, technolog, avail, will, today, full, unit, face, pass, compani, requir, cut, point, step, even, andor, earlier, design, past, result, pacif, sell, mani, reason, also, reserv, one, nation, time, consid, billion, percent, deregul, buy, industri, money, key, presid, day, set, remain, issu, success, plan, edison, call, accord, last, sever, own, earli, spot, includ, year, appear, bid, base, balanc, get, staff, anoth, deliveri, sinc, got, control, allow, now, resourc, jeff, small, articl, charg, per, washington, dollar, better, seller, contract, benefit, board, larg, wednesday, help, near, sold, term, turn, construct, five, much, like, around, amount, direct, period, top, structur, offici, governor, signific, meet, rather, end, friday, report, may, fact, dont, agenc, offer, use, follow, file, taken, purpos, opinion, talk, certain, grow, good, keep, altern, busi, dasovichnaenron, exist, come, make, real, drive, avoid, pge, monday, effort, effici, month, back, repres, measur, davi, determin, exchang, develop, lead, need, januari, payment, caus, immedi, least, run, explain, messag, havent, next., tuesday, occur, san, receiv, program, upon, start, refer, ago, prohibit, approxim, although, differ, releas, legisl, analysi, just, later, open, chairman, region, move, clear, administr, forward, post, director, decemb, produc, believ, estim, alreadi, fix, found, recent, tri, consult, facil, particip, trader, seek, see, novemb, crisi, cpuc, richard, far, entir, investig, polici, retail, indic, free, execut, respons, materi, modifi, X100, citi, X2001, steven, new, third, cover, oblig, long, portion, august, suggest, basi, idea, return, possibl, tariff, procedur, might, person, regul, readi, condit, agre, though, due, four, combin, X1999, big, someth, comput, look, averag, replac, everi, member, press, tom, record, applic, focus, way, hold, particular, short, decis, becom, jame, connect, corp, posit, thing, coupl, howev, court, manag, potenti, littl, juli, thursday, made, put, detail, begin, answer, site, well, must, total, excess, effect, enter, involv, complet, chanc, togeth, negoti, therefor, advanc, notic, ensur, brian, except, home, morn, give, act, storag, accept, coordin, unless, confer, otherwis, privat, futur, publish, share, fail, web, inform, given, arrang, that, statement, project, soon, current, second, question, first, week, william, ask, commod, say, hope, schedul, intend, jim, reach, less, case, outsid, daili, section, instead, name, place, rule, guarante, address, govern, associ, practic, subject, lot, access, trade, locat, susan, model, invest, main, build, depart, financ, note, left, identifi, internet, reflect, actual, express, bill, general, support, import, qualiti, confidenti, view, show, comment, peopl, eric, correct, work, similar, assum, fund, dasovich, late, sign, seem, seen, cash, option, tell, add, word, commerci, flow, sale, limit, attorney, previous, concern, submit, karen, approach, data, necessari, decid, various, recommend, abl, along, water, physic, claim, final, error, number, event, think, paul, alan, april, parti, exampl, els, group, anyon, dissemin, distribut, onlin, realli, south, delet, improv, sue, law, individu, oil, prepar, hear, leav, advis, head, standard, cynthia, joe, notifi, speak, thought, jan, tim, know, awar, strong, page, mean, sender, activ, organ, east, present, protect, corpor, west, kevin, enron, initi, singl, area, relat, kind, everyon, firm, electron, extend, assist, affect, octob, origin, specif, attent, septemb, deliv, chang, york, rob, hard, north, valu, termin, capit, offic, either, calcul, yet, ill, consider, side, approv, request, shall, asset, product, feel, let, carol, someon, march, probabl, sent, commit, appropri, matter, depend, research, chris, keannaenronenron, quick, contain, hand, gari, respect, steve, guy, date, deal, tomorrow, repli, latest, interest, extens, scott, financi, liquid, texa, local, intern, entiti, vinc, anyth, team, read, great, via, risk, impact, proceed, desk, ive, done, harri, llc, afternoon, mari, michael, cours, best, perform, link, delay, account, committe, opportun, privileg, process, find, sure, mail, greg, send, legal, recipi, london, transact, confirm, mention, summari, convers, prior, visit, respond, bit, yesterday, websit, format, review, paper, georg, peter, handl, kelli, item, employe, net, provis, februari, suit, frank, robert, attend, strategi, dave, email.1, street, join, dan, list, ken, met, appreci, draft, contact, status, bob, version, type, appli, sorri, mark, credit, understand, kaminskihouect, etc, rick, brief, communic, document, bank, sheet, phone, gerald, ena, copi, fyi, counsel, john, updat, jeffrey, roger, lesli, lisa, agreement, volum, letter, elizabeth, settlement, discuss, houston, global, info, transfer, pleas, america, europ, smith, stephani, memo, dear, regard, fax, form, counterparti, languag, mike, master, X77002, david, check, print, amend, eol, kate, attach, book, X1400, revis, thank"
## Fitting cp = 0 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 598
##
## CP nsplit rel error
## 1 0.29896907 0 1.0000000
## 2 0.06185567 1 0.7010309
## 3 0.02061856 3 0.5773196
## 4 0.00000000 6 0.5154639
##
## Variable importance
## california wholesal edison said southern util
## 23 8 7 7 7 7
## demand system bid jeff gas custom
## 7 4 3 3 2 2
## follow market natur question increas price
## 2 2 2 2 1 1
## richard continu electr produc begin cap
## 1 1 1 1 1 1
## citi san post
## 1 1 1
##
## Node number 1: 598 observations, complexity param=0.2989691
## predicted class=N expected loss=0.1622074 P(node) =1
## class counts: 501 97
## probabilities: 0.838 0.162
## left son=2 (547 obs) right son=3 (51 obs)
## Primary splits:
## california < 1.5 to the left, improve=43.15621, (0 missing)
## price < 1.5 to the left, improve=38.19520, (0 missing)
## electr < 1.5 to the left, improve=36.87857, (0 missing)
## state < 1.5 to the left, improve=33.95206, (0 missing)
## demand < 0.5 to the left, improve=31.57326, (0 missing)
## Surrogate splits:
## wholesal < 0.5 to the left, agree=0.945, adj=0.353, (0 split)
## southern < 0.5 to the left, agree=0.941, adj=0.314, (0 split)
## said < 2.5 to the left, agree=0.941, adj=0.314, (0 split)
## edison < 0.5 to the left, agree=0.941, adj=0.314, (0 split)
## util < 2.5 to the left, agree=0.940, adj=0.294, (0 split)
##
## Node number 2: 547 observations, complexity param=0.06185567
## predicted class=N expected loss=0.1042048 P(node) =0.9147157
## class counts: 490 57
## probabilities: 0.896 0.104
## left son=4 (515 obs) right son=5 (32 obs)
## Primary splits:
## demand < 0.5 to the left, improve=12.396750, (0 missing)
## system < 0.5 to the left, improve=10.501420, (0 missing)
## price < 6.5 to the left, improve= 8.715977, (0 missing)
## transmiss < 0.5 to the left, improve= 8.340129, (0 missing)
## bid < 0.5 to the left, improve= 8.337490, (0 missing)
## Surrogate splits:
## price < 6.5 to the left, agree=0.951, adj=0.156, (0 split)
## increas < 1.5 to the left, agree=0.951, adj=0.156, (0 split)
## continu < 1.5 to the left, agree=0.949, adj=0.125, (0 split)
## electr < 2.5 to the left, agree=0.949, adj=0.125, (0 split)
## produc < 1.5 to the left, agree=0.949, adj=0.125, (0 split)
##
## Node number 3: 51 observations
## predicted class=Y expected loss=0.2156863 P(node) =0.08528428
## class counts: 11 40
## probabilities: 0.216 0.784
##
## Node number 4: 515 observations, complexity param=0.02061856
## predicted class=N expected loss=0.0776699 P(node) =0.861204
## class counts: 475 40
## probabilities: 0.922 0.078
## left son=8 (505 obs) right son=9 (10 obs)
## Primary splits:
## bid < 0.5 to the left, improve=5.564626, (0 missing)
## gas < 1.5 to the left, improve=4.545434, (0 missing)
## andor < 0.5 to the left, improve=4.188857, (0 missing)
## offer < 0.5 to the left, improve=4.188857, (0 missing)
## capac < 0.5 to the left, improve=3.758748, (0 missing)
## Surrogate splits:
## post < 4.5 to the left, agree=0.984, adj=0.2, (0 split)
## addit < 2.5 to the left, agree=0.983, adj=0.1, (0 split)
## five < 1.5 to the left, agree=0.983, adj=0.1, (0 split)
## steven < 3 to the left, agree=0.983, adj=0.1, (0 split)
## section < 3.5 to the left, agree=0.983, adj=0.1, (0 split)
##
## Node number 5: 32 observations, complexity param=0.06185567
## predicted class=Y expected loss=0.46875 P(node) =0.05351171
## class counts: 15 17
## probabilities: 0.469 0.531
## left son=10 (16 obs) right son=11 (16 obs)
## Primary splits:
## system < 0.5 to the left, improve=7.562500, (0 missing)
## natur < 1.5 to the left, improve=3.937500, (0 missing)
## custom < 0.5 to the left, improve=2.760011, (0 missing)
## electr < 0.5 to the left, improve=2.760011, (0 missing)
## anyth < 0.5 to the right, improve=2.703214, (0 missing)
## Surrogate splits:
## custom < 0.5 to the left, agree=0.719, adj=0.438, (0 split)
## natur < 0.5 to the left, agree=0.719, adj=0.438, (0 split)
## market < 0.5 to the left, agree=0.719, adj=0.438, (0 split)
## follow < 0.5 to the left, agree=0.719, adj=0.438, (0 split)
## question < 0.5 to the left, agree=0.719, adj=0.438, (0 split)
##
## Node number 8: 505 observations, complexity param=0.02061856
## predicted class=N expected loss=0.06732673 P(node) =0.8444816
## class counts: 471 34
## probabilities: 0.933 0.067
## left son=16 (452 obs) right son=17 (53 obs)
## Primary splits:
## gas < 1.5 to the left, improve=4.587920, (0 missing)
## capac < 0.5 to the left, improve=4.039136, (0 missing)
## offer < 0.5 to the left, improve=3.791979, (0 missing)
## andor < 0.5 to the left, improve=3.565926, (0 missing)
## transmiss < 0.5 to the left, improve=3.100846, (0 missing)
## Surrogate splits:
## pipelin < 0.5 to the left, agree=0.911, adj=0.151, (0 split)
## volum < 2.5 to the left, agree=0.909, adj=0.132, (0 split)
## power < 3.5 to the left, agree=0.907, adj=0.113, (0 split)
## plant < 0.5 to the left, agree=0.907, adj=0.113, (0 split)
## X100 < 1.5 to the left, agree=0.907, adj=0.113, (0 split)
##
## Node number 9: 10 observations
## predicted class=Y expected loss=0.4 P(node) =0.01672241
## class counts: 4 6
## probabilities: 0.400 0.600
##
## Node number 10: 16 observations
## predicted class=N expected loss=0.1875 P(node) =0.02675585
## class counts: 13 3
## probabilities: 0.812 0.188
##
## Node number 11: 16 observations
## predicted class=Y expected loss=0.125 P(node) =0.02675585
## class counts: 2 14
## probabilities: 0.125 0.875
##
## Node number 16: 452 observations
## predicted class=N expected loss=0.04424779 P(node) =0.7558528
## class counts: 432 20
## probabilities: 0.956 0.044
##
## Node number 17: 53 observations, complexity param=0.02061856
## predicted class=N expected loss=0.2641509 P(node) =0.08862876
## class counts: 39 14
## probabilities: 0.736 0.264
## left son=34 (43 obs) right son=35 (10 obs)
## Primary splits:
## jeff < 0.5 to the left, improve=4.682843, (0 missing)
## custom < 0.5 to the left, improve=2.780518, (0 missing)
## follow < 0.5 to the right, improve=2.650213, (0 missing)
## andor < 0.5 to the left, improve=2.453774, (0 missing)
## good < 0.5 to the left, improve=2.453774, (0 missing)
## Surrogate splits:
## richard < 0.5 to the left, agree=0.887, adj=0.4, (0 split)
## cap < 0.5 to the left, agree=0.868, adj=0.3, (0 split)
## san < 1.5 to the left, agree=0.868, adj=0.3, (0 split)
## citi < 1.5 to the left, agree=0.868, adj=0.3, (0 split)
## begin < 0.5 to the left, agree=0.868, adj=0.3, (0 split)
##
## Node number 34: 43 observations
## predicted class=N expected loss=0.1627907 P(node) =0.07190635
## class counts: 36 7
## probabilities: 0.837 0.163
##
## Node number 35: 10 observations
## predicted class=Y expected loss=0.3 P(node) =0.01672241
## class counts: 3 7
## probabilities: 0.300 0.700
##
## n= 598
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 598 97 N (0.83779264 0.16220736)
## 2) california< 1.5 547 57 N (0.89579525 0.10420475)
## 4) demand< 0.5 515 40 N (0.92233010 0.07766990)
## 8) bid< 0.5 505 34 N (0.93267327 0.06732673)
## 16) gas< 1.5 452 20 N (0.95575221 0.04424779) *
## 17) gas>=1.5 53 14 N (0.73584906 0.26415094)
## 34) jeff< 0.5 43 7 N (0.83720930 0.16279070) *
## 35) jeff>=0.5 10 3 Y (0.30000000 0.70000000) *
## 9) bid>=0.5 10 4 Y (0.40000000 0.60000000) *
## 5) demand>=0.5 32 15 Y (0.46875000 0.53125000)
## 10) system< 0.5 16 3 N (0.81250000 0.18750000) *
## 11) system>=0.5 16 2 Y (0.12500000 0.87500000) *
## 3) california>=1.5 51 11 Y (0.21568627 0.78431373) *
## Reference
## Prediction N Y
## N 0 0
## Y 501 97
## responsive.fctr responsive.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 501
## 2 97
## Reference
## Prediction N Y
## N 432 20
## Y 69 77
## responsive.fctr responsive.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 432
## 2 Y 20
## responsive.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 69
## 2 77
## Reference
## Prediction N Y
## N 481 30
## Y 20 67
## responsive.fctr responsive.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 481
## 2 Y 30
## responsive.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 20
## 2 67
## Reference
## Prediction N Y
## N 481 30
## Y 20 67
## responsive.fctr responsive.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 481
## 2 Y 30
## responsive.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 20
## 2 67
## Reference
## Prediction N Y
## N 481 30
## Y 20 67
## responsive.fctr responsive.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 481
## 2 Y 30
## responsive.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 20
## 2 67
## Reference
## Prediction N Y
## N 481 30
## Y 20 67
## responsive.fctr responsive.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 481
## 2 Y 30
## responsive.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 20
## 2 67
## Reference
## Prediction N Y
## N 481 30
## Y 20 67
## responsive.fctr responsive.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 481
## 2 Y 30
## responsive.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 20
## 2 67
## Reference
## Prediction N Y
## N 488 43
## Y 13 54
## responsive.fctr responsive.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 488
## 2 Y 43
## responsive.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 13
## 2 54
## Reference
## Prediction N Y
## N 499 83
## Y 2 14
## responsive.fctr responsive.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 499
## 2 Y 83
## responsive.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 2
## 2 14
## Reference
## Prediction N Y
## N 501 97
## Y 0 0
## responsive.fctr responsive.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 501
## 2 Y 97
## responsive.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 0
## 2 0
## Reference
## Prediction N Y
## N 501 97
## Y 0 0
## responsive.fctr responsive.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 501
## 2 Y 97
## responsive.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2791367
## 2 0.1 0.6337449
## 3 0.2 0.7282609
## 4 0.3 0.7282609
## 5 0.4 0.7282609
## 6 0.5 0.7282609
## 7 0.6 0.7282609
## 8 0.7 0.6585366
## 9 0.8 0.2477876
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.fit"
## responsive.fctr responsive.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 481
## 2 Y 30
## responsive.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 20
## 2 67
## Reference
## Prediction N Y
## N 481 30
## Y 20 67
## responsive.fctr responsive.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 481
## 2 Y 30
## responsive.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 20
## 2 67
## Prediction
## Reference N Y
## N 481 20
## Y 30 67
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.163880e-01 6.790261e-01 8.912524e-01 9.373064e-01 8.377926e-01
## AccuracyPValue McnemarPValue
## 1.225206e-08 2.030918e-01
## Reference
## Prediction N Y
## N 0 0
## Y 215 42
## responsive.fctr responsive.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 215
## 2 42
## Reference
## Prediction N Y
## N 176 12
## Y 39 30
## responsive.fctr responsive.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 176
## 2 Y 12
## responsive.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 39
## 2 30
## Reference
## Prediction N Y
## N 195 17
## Y 20 25
## responsive.fctr responsive.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 195
## 2 Y 17
## responsive.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 20
## 2 25
## Reference
## Prediction N Y
## N 195 17
## Y 20 25
## responsive.fctr responsive.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 195
## 2 Y 17
## responsive.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 20
## 2 25
## Reference
## Prediction N Y
## N 195 17
## Y 20 25
## responsive.fctr responsive.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 195
## 2 Y 17
## responsive.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 20
## 2 25
## Reference
## Prediction N Y
## N 195 17
## Y 20 25
## responsive.fctr responsive.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 195
## 2 Y 17
## responsive.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 20
## 2 25
## Reference
## Prediction N Y
## N 195 17
## Y 20 25
## responsive.fctr responsive.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 195
## 2 Y 17
## responsive.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 20
## 2 25
## Reference
## Prediction N Y
## N 201 17
## Y 14 25
## responsive.fctr responsive.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 201
## 2 Y 17
## responsive.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 14
## 2 25
## Reference
## Prediction N Y
## N 212 40
## Y 3 2
## responsive.fctr responsive.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 212
## 2 Y 40
## responsive.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 3
## 2 2
## Reference
## Prediction N Y
## N 215 42
## Y 0 0
## responsive.fctr responsive.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 215
## 2 Y 42
## responsive.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 0
## 2 0
## Reference
## Prediction N Y
## N 215 42
## Y 0 0
## responsive.fctr responsive.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 215
## 2 Y 42
## responsive.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.28093645
## 2 0.1 0.54054054
## 3 0.2 0.57471264
## 4 0.3 0.57471264
## 5 0.4 0.57471264
## 6 0.5 0.57471264
## 7 0.6 0.57471264
## 8 0.7 0.61728395
## 9 0.8 0.08510638
## 10 0.9 0.00000000
## 11 1.0 0.00000000## [1] "Classifier Probability Threshold: 0.7000 to maximize f.score.OOB"
## responsive.fctr responsive.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 201
## 2 Y 17
## responsive.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 14
## 2 25
## Reference
## Prediction N Y
## N 201 17
## Y 14 25
## responsive.fctr responsive.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 201
## 2 Y 17
## responsive.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 14
## 2 25
## Prediction
## Reference N Y
## N 201 14
## Y 17 25
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.8793774 0.5458070 0.8331724 0.9165525 0.8365759
## AccuracyPValue McnemarPValue
## 0.0345096 0.7194375
## model_id model_method
## 1 Conditional.X.cp.0.rpart rpart
## feats
## 1 price, independ, demand, generat, suppli, emerg, summer, ferc, southern, util, custom, dow, high, oper, california, transmiss, natur, power, iso, jone, forc, gas, longer, capac, low, increas, expect, problem, consum, pay, system, plant, take, higher, rate, feder, said, continu, cap, commiss, sourc, solut, three, among, right, addit, order, author, line, energi, bring, push, major, load, told, can, lower, public, pipelin, peak, cost, supplier, without, electr, state, copyright, hour, interconnect, econom, two, implement, analyst, june, million, level, close, establish, want, within, purchas, other, part, announc, enough, servic, market, inc, grid, provid, competit, reduc, regulatori, news, steffesnaenronenron, creat, propos, still, situat, profit, X2000, whether, wholesal, transport, paid, fall, action, serv, rais, fuel, technolog, avail, will, today, full, unit, face, pass, compani, requir, cut, point, step, even, andor, earlier, design, past, result, pacif, sell, mani, reason, also, reserv, one, nation, time, consid, billion, percent, deregul, buy, industri, money, key, presid, day, set, remain, issu, success, plan, edison, call, accord, last, sever, own, earli, spot, includ, year, appear, bid, base, balanc, get, staff, anoth, deliveri, sinc, got, control, allow, now, resourc, jeff, small, articl, charg, per, washington, dollar, better, seller, contract, benefit, board, larg, wednesday, help, near, sold, term, turn, construct, five, much, like, around, amount, direct, period, top, structur, offici, governor, signific, meet, rather, end, friday, report, may, fact, dont, agenc, offer, use, follow, file, taken, purpos, opinion, talk, certain, grow, good, keep, altern, busi, dasovichnaenron, exist, come, make, real, drive, avoid, pge, monday, effort, effici, month, back, repres, measur, davi, determin, exchang, develop, lead, need, januari, payment, caus, immedi, least, run, explain, messag, havent, next., tuesday, occur, san, receiv, program, upon, start, refer, ago, prohibit, approxim, although, differ, releas, legisl, analysi, just, later, open, chairman, region, move, clear, administr, forward, post, director, decemb, produc, believ, estim, alreadi, fix, found, recent, tri, consult, facil, particip, trader, seek, see, novemb, crisi, cpuc, richard, far, entir, investig, polici, retail, indic, free, execut, respons, materi, modifi, X100, citi, X2001, steven, new, third, cover, oblig, long, portion, august, suggest, basi, idea, return, possibl, tariff, procedur, might, person, regul, readi, condit, agre, though, due, four, combin, X1999, big, someth, comput, look, averag, replac, everi, member, press, tom, record, applic, focus, way, hold, particular, short, decis, becom, jame, connect, corp, posit, thing, coupl, howev, court, manag, potenti, littl, juli, thursday, made, put, detail, begin, answer, site, well, must, total, excess, effect, enter, involv, complet, chanc, togeth, negoti, therefor, advanc, notic, ensur, brian, except, home, morn, give, act, storag, accept, coordin, unless, confer, otherwis, privat, futur, publish, share, fail, web, inform, given, arrang, that, statement, project, soon, current, second, question, first, week, william, ask, commod, say, hope, schedul, intend, jim, reach, less, case, outsid, daili, section, instead, name, place, rule, guarante, address, govern, associ, practic, subject, lot, access, trade, locat, susan, model, invest, main, build, depart, financ, note, left, identifi, internet, reflect, actual, express, bill, general, support, import, qualiti, confidenti, view, show, comment, peopl, eric, correct, work, similar, assum, fund, dasovich, late, sign, seem, seen, cash, option, tell, add, word, commerci, flow, sale, limit, attorney, previous, concern, submit, karen, approach, data, necessari, decid, various, recommend, abl, along, water, physic, claim, final, error, number, event, think, paul, alan, april, parti, exampl, els, group, anyon, dissemin, distribut, onlin, realli, south, delet, improv, sue, law, individu, oil, prepar, hear, leav, advis, head, standard, cynthia, joe, notifi, speak, thought, jan, tim, know, awar, strong, page, mean, sender, activ, organ, east, present, protect, corpor, west, kevin, enron, initi, singl, area, relat, kind, everyon, firm, electron, extend, assist, affect, octob, origin, specif, attent, septemb, deliv, chang, york, rob, hard, north, valu, termin, capit, offic, either, calcul, yet, ill, consider, side, approv, request, shall, asset, product, feel, let, carol, someon, march, probabl, sent, commit, appropri, matter, depend, research, chris, keannaenronenron, quick, contain, hand, gari, respect, steve, guy, date, deal, tomorrow, repli, latest, interest, extens, scott, financi, liquid, texa, local, intern, entiti, vinc, anyth, team, read, great, via, risk, impact, proceed, desk, ive, done, harri, llc, afternoon, mari, michael, cours, best, perform, link, delay, account, committe, opportun, privileg, process, find, sure, mail, greg, send, legal, recipi, london, transact, confirm, mention, summari, convers, prior, visit, respond, bit, yesterday, websit, format, review, paper, georg, peter, handl, kelli, item, employe, net, provis, februari, suit, frank, robert, attend, strategi, dave, email.1, street, join, dan, list, ken, met, appreci, draft, contact, status, bob, version, type, appli, sorri, mark, credit, understand, kaminskihouect, etc, rick, brief, communic, document, bank, sheet, phone, gerald, ena, copi, fyi, counsel, john, updat, jeffrey, roger, lesli, lisa, agreement, volum, letter, elizabeth, settlement, discuss, houston, global, info, transfer, pleas, america, europ, smith, stephani, memo, dear, regard, fax, form, counterparti, languag, mike, master, X77002, david, check, print, amend, eol, kate, attach, book, X1400, revis, thank
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 1.638 1.145
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8629545 0.6 0.7282609 0.916388
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8912524 0.9373064 0.6790261 0.7936323
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.7 0.617284 0.8793774
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8331724 0.9165525 0.545807
## [1] "fitting model: Conditional.X.rf"
## [1] " indep_vars: price, independ, demand, generat, suppli, emerg, summer, ferc, southern, util, custom, dow, high, oper, california, transmiss, natur, power, iso, jone, forc, gas, longer, capac, low, increas, expect, problem, consum, pay, system, plant, take, higher, rate, feder, said, continu, cap, commiss, sourc, solut, three, among, right, addit, order, author, line, energi, bring, push, major, load, told, can, lower, public, pipelin, peak, cost, supplier, without, electr, state, copyright, hour, interconnect, econom, two, implement, analyst, june, million, level, close, establish, want, within, purchas, other, part, announc, enough, servic, market, inc, grid, provid, competit, reduc, regulatori, news, steffesnaenronenron, creat, propos, still, situat, profit, X2000, whether, wholesal, transport, paid, fall, action, serv, rais, fuel, technolog, avail, will, today, full, unit, face, pass, compani, requir, cut, point, step, even, andor, earlier, design, past, result, pacif, sell, mani, reason, also, reserv, one, nation, time, consid, billion, percent, deregul, buy, industri, money, key, presid, day, set, remain, issu, success, plan, edison, call, accord, last, sever, own, earli, spot, includ, year, appear, bid, base, balanc, get, staff, anoth, deliveri, sinc, got, control, allow, now, resourc, jeff, small, articl, charg, per, washington, dollar, better, seller, contract, benefit, board, larg, wednesday, help, near, sold, term, turn, construct, five, much, like, around, amount, direct, period, top, structur, offici, governor, signific, meet, rather, end, friday, report, may, fact, dont, agenc, offer, use, follow, file, taken, purpos, opinion, talk, certain, grow, good, keep, altern, busi, dasovichnaenron, exist, come, make, real, drive, avoid, pge, monday, effort, effici, month, back, repres, measur, davi, determin, exchang, develop, lead, need, januari, payment, caus, immedi, least, run, explain, messag, havent, next., tuesday, occur, san, receiv, program, upon, start, refer, ago, prohibit, approxim, although, differ, releas, legisl, analysi, just, later, open, chairman, region, move, clear, administr, forward, post, director, decemb, produc, believ, estim, alreadi, fix, found, recent, tri, consult, facil, particip, trader, seek, see, novemb, crisi, cpuc, richard, far, entir, investig, polici, retail, indic, free, execut, respons, materi, modifi, X100, citi, X2001, steven, new, third, cover, oblig, long, portion, august, suggest, basi, idea, return, possibl, tariff, procedur, might, person, regul, readi, condit, agre, though, due, four, combin, X1999, big, someth, comput, look, averag, replac, everi, member, press, tom, record, applic, focus, way, hold, particular, short, decis, becom, jame, connect, corp, posit, thing, coupl, howev, court, manag, potenti, littl, juli, thursday, made, put, detail, begin, answer, site, well, must, total, excess, effect, enter, involv, complet, chanc, togeth, negoti, therefor, advanc, notic, ensur, brian, except, home, morn, give, act, storag, accept, coordin, unless, confer, otherwis, privat, futur, publish, share, fail, web, inform, given, arrang, that, statement, project, soon, current, second, question, first, week, william, ask, commod, say, hope, schedul, intend, jim, reach, less, case, outsid, daili, section, instead, name, place, rule, guarante, address, govern, associ, practic, subject, lot, access, trade, locat, susan, model, invest, main, build, depart, financ, note, left, identifi, internet, reflect, actual, express, bill, general, support, import, qualiti, confidenti, view, show, comment, peopl, eric, correct, work, similar, assum, fund, dasovich, late, sign, seem, seen, cash, option, tell, add, word, commerci, flow, sale, limit, attorney, previous, concern, submit, karen, approach, data, necessari, decid, various, recommend, abl, along, water, physic, claim, final, error, number, event, think, paul, alan, april, parti, exampl, els, group, anyon, dissemin, distribut, onlin, realli, south, delet, improv, sue, law, individu, oil, prepar, hear, leav, advis, head, standard, cynthia, joe, notifi, speak, thought, jan, tim, know, awar, strong, page, mean, sender, activ, organ, east, present, protect, corpor, west, kevin, enron, initi, singl, area, relat, kind, everyon, firm, electron, extend, assist, affect, octob, origin, specif, attent, septemb, deliv, chang, york, rob, hard, north, valu, termin, capit, offic, either, calcul, yet, ill, consider, side, approv, request, shall, asset, product, feel, let, carol, someon, march, probabl, sent, commit, appropri, matter, depend, research, chris, keannaenronenron, quick, contain, hand, gari, respect, steve, guy, date, deal, tomorrow, repli, latest, interest, extens, scott, financi, liquid, texa, local, intern, entiti, vinc, anyth, team, read, great, via, risk, impact, proceed, desk, ive, done, harri, llc, afternoon, mari, michael, cours, best, perform, link, delay, account, committe, opportun, privileg, process, find, sure, mail, greg, send, legal, recipi, london, transact, confirm, mention, summari, convers, prior, visit, respond, bit, yesterday, websit, format, review, paper, georg, peter, handl, kelli, item, employe, net, provis, februari, suit, frank, robert, attend, strategi, dave, email.1, street, join, dan, list, ken, met, appreci, draft, contact, status, bob, version, type, appli, sorri, mark, credit, understand, kaminskihouect, etc, rick, brief, communic, document, bank, sheet, phone, gerald, ena, copi, fyi, counsel, john, updat, jeffrey, roger, lesli, lisa, agreement, volum, letter, elizabeth, .rnorm, settlement, discuss, houston, global, info, transfer, pleas, america, europ, smith, stephani, memo, dear, regard, fax, form, counterparti, languag, mike, master, X77002, david, check, print, amend, eol, kate, attach, book, X1400, revis, thank"## Loading required package: randomForest
## randomForest 4.6-10
## Type rfNews() to see new features/changes/bug fixes.
## + : mtry= 2
## - : mtry= 2
## + : mtry= 39
## - : mtry= 39
## + : mtry=783
## - : mtry=783
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 783 on full training set## Warning in myfit_mdl(model_id = paste0(model_id_pfx, ""), model_method =
## method, : model's bestTune found at an extreme of tuneGrid for parameter:
## mtry
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 598 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 1196 matrix numeric
## oob.times 598 -none- numeric
## classes 2 -none- character
## importance 783 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 598 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 783 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## Reference
## Prediction N Y
## N 0 0
## Y 501 97
## responsive.fctr responsive.fctr.predict.Conditional.X.rf.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Conditional.X.rf.Y
## 1 501
## 2 97
## Reference
## Prediction N Y
## N 433 0
## Y 68 97
## responsive.fctr responsive.fctr.predict.Conditional.X.rf.N
## 1 N 433
## 2 Y 0
## responsive.fctr.predict.Conditional.X.rf.Y
## 1 68
## 2 97
## Reference
## Prediction N Y
## N 484 0
## Y 17 97
## responsive.fctr responsive.fctr.predict.Conditional.X.rf.N
## 1 N 484
## 2 Y 0
## responsive.fctr.predict.Conditional.X.rf.Y
## 1 17
## 2 97
## Reference
## Prediction N Y
## N 500 0
## Y 1 97
## responsive.fctr responsive.fctr.predict.Conditional.X.rf.N
## 1 N 500
## 2 Y 0
## responsive.fctr.predict.Conditional.X.rf.Y
## 1 1
## 2 97
## Reference
## Prediction N Y
## N 501 0
## Y 0 97
## responsive.fctr responsive.fctr.predict.Conditional.X.rf.N
## 1 N 501
## 2 Y 0
## responsive.fctr.predict.Conditional.X.rf.Y
## 1 0
## 2 97
## Reference
## Prediction N Y
## N 501 0
## Y 0 97
## responsive.fctr responsive.fctr.predict.Conditional.X.rf.N
## 1 N 501
## 2 Y 0
## responsive.fctr.predict.Conditional.X.rf.Y
## 1 0
## 2 97
## Reference
## Prediction N Y
## N 501 1
## Y 0 96
## responsive.fctr responsive.fctr.predict.Conditional.X.rf.N
## 1 N 501
## 2 Y 1
## responsive.fctr.predict.Conditional.X.rf.Y
## 1 0
## 2 96
## Reference
## Prediction N Y
## N 501 25
## Y 0 72
## responsive.fctr responsive.fctr.predict.Conditional.X.rf.N
## 1 N 501
## 2 Y 25
## responsive.fctr.predict.Conditional.X.rf.Y
## 1 0
## 2 72
## Reference
## Prediction N Y
## N 501 46
## Y 0 51
## responsive.fctr responsive.fctr.predict.Conditional.X.rf.N
## 1 N 501
## 2 Y 46
## responsive.fctr.predict.Conditional.X.rf.Y
## 1 0
## 2 51
## Reference
## Prediction N Y
## N 501 70
## Y 0 27
## responsive.fctr responsive.fctr.predict.Conditional.X.rf.N
## 1 N 501
## 2 Y 70
## responsive.fctr.predict.Conditional.X.rf.Y
## 1 0
## 2 27
## Reference
## Prediction N Y
## N 501 97
## Y 0 0
## responsive.fctr responsive.fctr.predict.Conditional.X.rf.N
## 1 N 501
## 2 Y 97
## responsive.fctr.predict.Conditional.X.rf.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2791367
## 2 0.1 0.7404580
## 3 0.2 0.9194313
## 4 0.3 0.9948718
## 5 0.4 1.0000000
## 6 0.5 1.0000000
## 7 0.6 0.9948187
## 8 0.7 0.8520710
## 9 0.8 0.6891892
## 10 0.9 0.4354839
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## responsive.fctr responsive.fctr.predict.Conditional.X.rf.N
## 1 N 501
## 2 Y NA
## responsive.fctr.predict.Conditional.X.rf.Y
## 1 NA
## 2 97
## Reference
## Prediction N Y
## N 501 0
## Y 0 97
## responsive.fctr responsive.fctr.predict.Conditional.X.rf.N
## 1 N 501
## 2 Y 0
## responsive.fctr.predict.Conditional.X.rf.Y
## 1 0
## 2 97
## Prediction
## Reference N Y
## N 501 0
## Y 0 97
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 1.000000e+00 1.000000e+00 9.938503e-01 1.000000e+00 8.377926e-01
## AccuracyPValue McnemarPValue
## 1.085556e-46 NaN
## Reference
## Prediction N Y
## N 0 0
## Y 215 42
## responsive.fctr responsive.fctr.predict.Conditional.X.rf.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Conditional.X.rf.Y
## 1 215
## 2 42
## Reference
## Prediction N Y
## N 152 1
## Y 63 41
## responsive.fctr responsive.fctr.predict.Conditional.X.rf.N
## 1 N 152
## 2 Y 1
## responsive.fctr.predict.Conditional.X.rf.Y
## 1 63
## 2 41
## Reference
## Prediction N Y
## N 169 4
## Y 46 38
## responsive.fctr responsive.fctr.predict.Conditional.X.rf.N
## 1 N 169
## 2 Y 4
## responsive.fctr.predict.Conditional.X.rf.Y
## 1 46
## 2 38
## Reference
## Prediction N Y
## N 182 10
## Y 33 32
## responsive.fctr responsive.fctr.predict.Conditional.X.rf.N
## 1 N 182
## 2 Y 10
## responsive.fctr.predict.Conditional.X.rf.Y
## 1 33
## 2 32
## Reference
## Prediction N Y
## N 190 12
## Y 25 30
## responsive.fctr responsive.fctr.predict.Conditional.X.rf.N
## 1 N 190
## 2 Y 12
## responsive.fctr.predict.Conditional.X.rf.Y
## 1 25
## 2 30
## Reference
## Prediction N Y
## N 196 16
## Y 19 26
## responsive.fctr responsive.fctr.predict.Conditional.X.rf.N
## 1 N 196
## 2 Y 16
## responsive.fctr.predict.Conditional.X.rf.Y
## 1 19
## 2 26
## Reference
## Prediction N Y
## N 205 21
## Y 10 21
## responsive.fctr responsive.fctr.predict.Conditional.X.rf.N
## 1 N 205
## 2 Y 21
## responsive.fctr.predict.Conditional.X.rf.Y
## 1 10
## 2 21
## Reference
## Prediction N Y
## N 213 25
## Y 2 17
## responsive.fctr responsive.fctr.predict.Conditional.X.rf.N
## 1 N 213
## 2 Y 25
## responsive.fctr.predict.Conditional.X.rf.Y
## 1 2
## 2 17
## Reference
## Prediction N Y
## N 214 30
## Y 1 12
## responsive.fctr responsive.fctr.predict.Conditional.X.rf.N
## 1 N 214
## 2 Y 30
## responsive.fctr.predict.Conditional.X.rf.Y
## 1 1
## 2 12
## Reference
## Prediction N Y
## N 214 41
## Y 1 1
## responsive.fctr responsive.fctr.predict.Conditional.X.rf.N
## 1 N 214
## 2 Y 41
## responsive.fctr.predict.Conditional.X.rf.Y
## 1 1
## 2 1
## Reference
## Prediction N Y
## N 215 42
## Y 0 0
## responsive.fctr responsive.fctr.predict.Conditional.X.rf.N
## 1 N 215
## 2 Y 42
## responsive.fctr.predict.Conditional.X.rf.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.28093645
## 2 0.1 0.56164384
## 3 0.2 0.60317460
## 4 0.3 0.59813084
## 5 0.4 0.61855670
## 6 0.5 0.59770115
## 7 0.6 0.57534247
## 8 0.7 0.55737705
## 9 0.8 0.43636364
## 10 0.9 0.04545455
## 11 1.0 0.00000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.OOB"
## responsive.fctr responsive.fctr.predict.Conditional.X.rf.N
## 1 N 190
## 2 Y 12
## responsive.fctr.predict.Conditional.X.rf.Y
## 1 25
## 2 30
## Reference
## Prediction N Y
## N 190 12
## Y 25 30
## responsive.fctr responsive.fctr.predict.Conditional.X.rf.N
## 1 N 190
## 2 Y 12
## responsive.fctr.predict.Conditional.X.rf.Y
## 1 25
## 2 30
## Prediction
## Reference N Y
## N 190 25
## Y 12 30
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.85603113 0.53178394 0.80705883 0.89656395 0.83657588
## AccuracyPValue McnemarPValue
## 0.22629677 0.04851974
## model_id model_method
## 1 Conditional.X.rf rf
## feats
## 1 price, independ, demand, generat, suppli, emerg, summer, ferc, southern, util, custom, dow, high, oper, california, transmiss, natur, power, iso, jone, forc, gas, longer, capac, low, increas, expect, problem, consum, pay, system, plant, take, higher, rate, feder, said, continu, cap, commiss, sourc, solut, three, among, right, addit, order, author, line, energi, bring, push, major, load, told, can, lower, public, pipelin, peak, cost, supplier, without, electr, state, copyright, hour, interconnect, econom, two, implement, analyst, june, million, level, close, establish, want, within, purchas, other, part, announc, enough, servic, market, inc, grid, provid, competit, reduc, regulatori, news, steffesnaenronenron, creat, propos, still, situat, profit, X2000, whether, wholesal, transport, paid, fall, action, serv, rais, fuel, technolog, avail, will, today, full, unit, face, pass, compani, requir, cut, point, step, even, andor, earlier, design, past, result, pacif, sell, mani, reason, also, reserv, one, nation, time, consid, billion, percent, deregul, buy, industri, money, key, presid, day, set, remain, issu, success, plan, edison, call, accord, last, sever, own, earli, spot, includ, year, appear, bid, base, balanc, get, staff, anoth, deliveri, sinc, got, control, allow, now, resourc, jeff, small, articl, charg, per, washington, dollar, better, seller, contract, benefit, board, larg, wednesday, help, near, sold, term, turn, construct, five, much, like, around, amount, direct, period, top, structur, offici, governor, signific, meet, rather, end, friday, report, may, fact, dont, agenc, offer, use, follow, file, taken, purpos, opinion, talk, certain, grow, good, keep, altern, busi, dasovichnaenron, exist, come, make, real, drive, avoid, pge, monday, effort, effici, month, back, repres, measur, davi, determin, exchang, develop, lead, need, januari, payment, caus, immedi, least, run, explain, messag, havent, next., tuesday, occur, san, receiv, program, upon, start, refer, ago, prohibit, approxim, although, differ, releas, legisl, analysi, just, later, open, chairman, region, move, clear, administr, forward, post, director, decemb, produc, believ, estim, alreadi, fix, found, recent, tri, consult, facil, particip, trader, seek, see, novemb, crisi, cpuc, richard, far, entir, investig, polici, retail, indic, free, execut, respons, materi, modifi, X100, citi, X2001, steven, new, third, cover, oblig, long, portion, august, suggest, basi, idea, return, possibl, tariff, procedur, might, person, regul, readi, condit, agre, though, due, four, combin, X1999, big, someth, comput, look, averag, replac, everi, member, press, tom, record, applic, focus, way, hold, particular, short, decis, becom, jame, connect, corp, posit, thing, coupl, howev, court, manag, potenti, littl, juli, thursday, made, put, detail, begin, answer, site, well, must, total, excess, effect, enter, involv, complet, chanc, togeth, negoti, therefor, advanc, notic, ensur, brian, except, home, morn, give, act, storag, accept, coordin, unless, confer, otherwis, privat, futur, publish, share, fail, web, inform, given, arrang, that, statement, project, soon, current, second, question, first, week, william, ask, commod, say, hope, schedul, intend, jim, reach, less, case, outsid, daili, section, instead, name, place, rule, guarante, address, govern, associ, practic, subject, lot, access, trade, locat, susan, model, invest, main, build, depart, financ, note, left, identifi, internet, reflect, actual, express, bill, general, support, import, qualiti, confidenti, view, show, comment, peopl, eric, correct, work, similar, assum, fund, dasovich, late, sign, seem, seen, cash, option, tell, add, word, commerci, flow, sale, limit, attorney, previous, concern, submit, karen, approach, data, necessari, decid, various, recommend, abl, along, water, physic, claim, final, error, number, event, think, paul, alan, april, parti, exampl, els, group, anyon, dissemin, distribut, onlin, realli, south, delet, improv, sue, law, individu, oil, prepar, hear, leav, advis, head, standard, cynthia, joe, notifi, speak, thought, jan, tim, know, awar, strong, page, mean, sender, activ, organ, east, present, protect, corpor, west, kevin, enron, initi, singl, area, relat, kind, everyon, firm, electron, extend, assist, affect, octob, origin, specif, attent, septemb, deliv, chang, york, rob, hard, north, valu, termin, capit, offic, either, calcul, yet, ill, consider, side, approv, request, shall, asset, product, feel, let, carol, someon, march, probabl, sent, commit, appropri, matter, depend, research, chris, keannaenronenron, quick, contain, hand, gari, respect, steve, guy, date, deal, tomorrow, repli, latest, interest, extens, scott, financi, liquid, texa, local, intern, entiti, vinc, anyth, team, read, great, via, risk, impact, proceed, desk, ive, done, harri, llc, afternoon, mari, michael, cours, best, perform, link, delay, account, committe, opportun, privileg, process, find, sure, mail, greg, send, legal, recipi, london, transact, confirm, mention, summari, convers, prior, visit, respond, bit, yesterday, websit, format, review, paper, georg, peter, handl, kelli, item, employe, net, provis, februari, suit, frank, robert, attend, strategi, dave, email.1, street, join, dan, list, ken, met, appreci, draft, contact, status, bob, version, type, appli, sorri, mark, credit, understand, kaminskihouect, etc, rick, brief, communic, document, bank, sheet, phone, gerald, ena, copi, fyi, counsel, john, updat, jeffrey, roger, lesli, lisa, agreement, volum, letter, elizabeth, .rnorm, settlement, discuss, houston, global, info, transfer, pleas, america, europ, smith, stephani, memo, dear, regard, fax, form, counterparti, languag, mike, master, X77002, david, check, print, amend, eol, kate, attach, book, X1400, revis, thank
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 166.977 58.827
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 1 0.5 1 0.8879599
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.9938503 1 0.5267982 0.9187708
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.6185567 0.8560311
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8070588 0.896564 0.5317839
## [1] "fitting model: Conditional.X.no.rnorm.rf"
## [1] " indep_vars: price, independ, demand, generat, suppli, emerg, summer, ferc, southern, util, custom, dow, high, oper, california, transmiss, natur, power, iso, jone, forc, gas, longer, capac, low, increas, expect, problem, consum, pay, system, plant, take, higher, rate, feder, said, continu, cap, commiss, sourc, solut, three, among, right, addit, order, author, line, energi, bring, push, major, load, told, can, lower, public, pipelin, peak, cost, supplier, without, electr, state, copyright, hour, interconnect, econom, two, implement, analyst, june, million, level, close, establish, want, within, purchas, other, part, announc, enough, servic, market, inc, grid, provid, competit, reduc, regulatori, news, steffesnaenronenron, creat, propos, still, situat, profit, X2000, whether, wholesal, transport, paid, fall, action, serv, rais, fuel, technolog, avail, will, today, full, unit, face, pass, compani, requir, cut, point, step, even, andor, earlier, design, past, result, pacif, sell, mani, reason, also, reserv, one, nation, time, consid, billion, percent, deregul, buy, industri, money, key, presid, day, set, remain, issu, success, plan, edison, call, accord, last, sever, own, earli, spot, includ, year, appear, bid, base, balanc, get, staff, anoth, deliveri, sinc, got, control, allow, now, resourc, jeff, small, articl, charg, per, washington, dollar, better, seller, contract, benefit, board, larg, wednesday, help, near, sold, term, turn, construct, five, much, like, around, amount, direct, period, top, structur, offici, governor, signific, meet, rather, end, friday, report, may, fact, dont, agenc, offer, use, follow, file, taken, purpos, opinion, talk, certain, grow, good, keep, altern, busi, dasovichnaenron, exist, come, make, real, drive, avoid, pge, monday, effort, effici, month, back, repres, measur, davi, determin, exchang, develop, lead, need, januari, payment, caus, immedi, least, run, explain, messag, havent, next., tuesday, occur, san, receiv, program, upon, start, refer, ago, prohibit, approxim, although, differ, releas, legisl, analysi, just, later, open, chairman, region, move, clear, administr, forward, post, director, decemb, produc, believ, estim, alreadi, fix, found, recent, tri, consult, facil, particip, trader, seek, see, novemb, crisi, cpuc, richard, far, entir, investig, polici, retail, indic, free, execut, respons, materi, modifi, X100, citi, X2001, steven, new, third, cover, oblig, long, portion, august, suggest, basi, idea, return, possibl, tariff, procedur, might, person, regul, readi, condit, agre, though, due, four, combin, X1999, big, someth, comput, look, averag, replac, everi, member, press, tom, record, applic, focus, way, hold, particular, short, decis, becom, jame, connect, corp, posit, thing, coupl, howev, court, manag, potenti, littl, juli, thursday, made, put, detail, begin, answer, site, well, must, total, excess, effect, enter, involv, complet, chanc, togeth, negoti, therefor, advanc, notic, ensur, brian, except, home, morn, give, act, storag, accept, coordin, unless, confer, otherwis, privat, futur, publish, share, fail, web, inform, given, arrang, that, statement, project, soon, current, second, question, first, week, william, ask, commod, say, hope, schedul, intend, jim, reach, less, case, outsid, daili, section, instead, name, place, rule, guarante, address, govern, associ, practic, subject, lot, access, trade, locat, susan, model, invest, main, build, depart, financ, note, left, identifi, internet, reflect, actual, express, bill, general, support, import, qualiti, confidenti, view, show, comment, peopl, eric, correct, work, similar, assum, fund, dasovich, late, sign, seem, seen, cash, option, tell, add, word, commerci, flow, sale, limit, attorney, previous, concern, submit, karen, approach, data, necessari, decid, various, recommend, abl, along, water, physic, claim, final, error, number, event, think, paul, alan, april, parti, exampl, els, group, anyon, dissemin, distribut, onlin, realli, south, delet, improv, sue, law, individu, oil, prepar, hear, leav, advis, head, standard, cynthia, joe, notifi, speak, thought, jan, tim, know, awar, strong, page, mean, sender, activ, organ, east, present, protect, corpor, west, kevin, enron, initi, singl, area, relat, kind, everyon, firm, electron, extend, assist, affect, octob, origin, specif, attent, septemb, deliv, chang, york, rob, hard, north, valu, termin, capit, offic, either, calcul, yet, ill, consider, side, approv, request, shall, asset, product, feel, let, carol, someon, march, probabl, sent, commit, appropri, matter, depend, research, chris, keannaenronenron, quick, contain, hand, gari, respect, steve, guy, date, deal, tomorrow, repli, latest, interest, extens, scott, financi, liquid, texa, local, intern, entiti, vinc, anyth, team, read, great, via, risk, impact, proceed, desk, ive, done, harri, llc, afternoon, mari, michael, cours, best, perform, link, delay, account, committe, opportun, privileg, process, find, sure, mail, greg, send, legal, recipi, london, transact, confirm, mention, summari, convers, prior, visit, respond, bit, yesterday, websit, format, review, paper, georg, peter, handl, kelli, item, employe, net, provis, februari, suit, frank, robert, attend, strategi, dave, email.1, street, join, dan, list, ken, met, appreci, draft, contact, status, bob, version, type, appli, sorri, mark, credit, understand, kaminskihouect, etc, rick, brief, communic, document, bank, sheet, phone, gerald, ena, copi, fyi, counsel, john, updat, jeffrey, roger, lesli, lisa, agreement, volum, letter, elizabeth, settlement, discuss, houston, global, info, transfer, pleas, america, europ, smith, stephani, memo, dear, regard, fax, form, counterparti, languag, mike, master, X77002, david, check, print, amend, eol, kate, attach, book, X1400, revis, thank"
## + : mtry= 2
## - : mtry= 2
## + : mtry= 39
## - : mtry= 39
## + : mtry=782
## - : mtry=782
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 782 on full training set## Warning in myfit_mdl(model_id = paste0(model_id_pfx, ".no.rnorm"),
## model_method = method, : model's bestTune found at an extreme of tuneGrid
## for parameter: mtry
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 598 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 1196 matrix numeric
## oob.times 598 -none- numeric
## classes 2 -none- character
## importance 782 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 598 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 782 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## Reference
## Prediction N Y
## N 0 0
## Y 501 97
## responsive.fctr responsive.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 501
## 2 97
## Reference
## Prediction N Y
## N 437 0
## Y 64 97
## responsive.fctr responsive.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 437
## 2 Y 0
## responsive.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 64
## 2 97
## Reference
## Prediction N Y
## N 485 0
## Y 16 97
## responsive.fctr responsive.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 485
## 2 Y 0
## responsive.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 16
## 2 97
## Reference
## Prediction N Y
## N 496 0
## Y 5 97
## responsive.fctr responsive.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 496
## 2 Y 0
## responsive.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 5
## 2 97
## Reference
## Prediction N Y
## N 501 1
## Y 0 96
## responsive.fctr responsive.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 501
## 2 Y 1
## responsive.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 0
## 2 96
## Reference
## Prediction N Y
## N 501 1
## Y 0 96
## responsive.fctr responsive.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 501
## 2 Y 1
## responsive.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 0
## 2 96
## Reference
## Prediction N Y
## N 501 1
## Y 0 96
## responsive.fctr responsive.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 501
## 2 Y 1
## responsive.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 0
## 2 96
## Reference
## Prediction N Y
## N 501 22
## Y 0 75
## responsive.fctr responsive.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 501
## 2 Y 22
## responsive.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 0
## 2 75
## Reference
## Prediction N Y
## N 501 46
## Y 0 51
## responsive.fctr responsive.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 501
## 2 Y 46
## responsive.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 0
## 2 51
## Reference
## Prediction N Y
## N 501 71
## Y 0 26
## responsive.fctr responsive.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 501
## 2 Y 71
## responsive.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 0
## 2 26
## Reference
## Prediction N Y
## N 501 97
## Y 0 0
## responsive.fctr responsive.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 501
## 2 Y 97
## responsive.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2791367
## 2 0.1 0.7519380
## 3 0.2 0.9238095
## 4 0.3 0.9748744
## 5 0.4 0.9948187
## 6 0.5 0.9948187
## 7 0.6 0.9948187
## 8 0.7 0.8720930
## 9 0.8 0.6891892
## 10 0.9 0.4227642
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.fit"
## responsive.fctr responsive.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 501
## 2 Y 1
## responsive.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 NA
## 2 96
## Reference
## Prediction N Y
## N 501 1
## Y 0 96
## responsive.fctr responsive.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 501
## 2 Y 1
## responsive.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 0
## 2 96
## Prediction
## Reference N Y
## N 501 0
## Y 1 96
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.983278e-01 9.938217e-01 9.907184e-01 9.999577e-01 8.377926e-01
## AccuracyPValue McnemarPValue
## 1.267717e-44 1.000000e+00
## Reference
## Prediction N Y
## N 0 0
## Y 215 42
## responsive.fctr responsive.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 215
## 2 42
## Reference
## Prediction N Y
## N 152 1
## Y 63 41
## responsive.fctr responsive.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 152
## 2 Y 1
## responsive.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 63
## 2 41
## Reference
## Prediction N Y
## N 169 4
## Y 46 38
## responsive.fctr responsive.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 169
## 2 Y 4
## responsive.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 46
## 2 38
## Reference
## Prediction N Y
## N 179 10
## Y 36 32
## responsive.fctr responsive.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 179
## 2 Y 10
## responsive.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 36
## 2 32
## Reference
## Prediction N Y
## N 190 12
## Y 25 30
## responsive.fctr responsive.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 190
## 2 Y 12
## responsive.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 25
## 2 30
## Reference
## Prediction N Y
## N 196 16
## Y 19 26
## responsive.fctr responsive.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 196
## 2 Y 16
## responsive.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 19
## 2 26
## Reference
## Prediction N Y
## N 205 22
## Y 10 20
## responsive.fctr responsive.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 205
## 2 Y 22
## responsive.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 10
## 2 20
## Reference
## Prediction N Y
## N 212 25
## Y 3 17
## responsive.fctr responsive.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 212
## 2 Y 25
## responsive.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 3
## 2 17
## Reference
## Prediction N Y
## N 214 30
## Y 1 12
## responsive.fctr responsive.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 214
## 2 Y 30
## responsive.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 1
## 2 12
## Reference
## Prediction N Y
## N 214 39
## Y 1 3
## responsive.fctr responsive.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 214
## 2 Y 39
## responsive.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 1
## 2 3
## Reference
## Prediction N Y
## N 215 42
## Y 0 0
## responsive.fctr responsive.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 215
## 2 Y 42
## responsive.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2809365
## 2 0.1 0.5616438
## 3 0.2 0.6031746
## 4 0.3 0.5818182
## 5 0.4 0.6185567
## 6 0.5 0.5977011
## 7 0.6 0.5555556
## 8 0.7 0.5483871
## 9 0.8 0.4363636
## 10 0.9 0.1304348
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.OOB"
## responsive.fctr responsive.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 190
## 2 Y 12
## responsive.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 25
## 2 30
## Reference
## Prediction N Y
## N 190 12
## Y 25 30
## responsive.fctr responsive.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 190
## 2 Y 12
## responsive.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 25
## 2 30
## Prediction
## Reference N Y
## N 190 25
## Y 12 30
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.85603113 0.53178394 0.80705883 0.89656395 0.83657588
## AccuracyPValue McnemarPValue
## 0.22629677 0.04851974
## model_id model_method
## 1 Conditional.X.no.rnorm.rf rf
## feats
## 1 price, independ, demand, generat, suppli, emerg, summer, ferc, southern, util, custom, dow, high, oper, california, transmiss, natur, power, iso, jone, forc, gas, longer, capac, low, increas, expect, problem, consum, pay, system, plant, take, higher, rate, feder, said, continu, cap, commiss, sourc, solut, three, among, right, addit, order, author, line, energi, bring, push, major, load, told, can, lower, public, pipelin, peak, cost, supplier, without, electr, state, copyright, hour, interconnect, econom, two, implement, analyst, june, million, level, close, establish, want, within, purchas, other, part, announc, enough, servic, market, inc, grid, provid, competit, reduc, regulatori, news, steffesnaenronenron, creat, propos, still, situat, profit, X2000, whether, wholesal, transport, paid, fall, action, serv, rais, fuel, technolog, avail, will, today, full, unit, face, pass, compani, requir, cut, point, step, even, andor, earlier, design, past, result, pacif, sell, mani, reason, also, reserv, one, nation, time, consid, billion, percent, deregul, buy, industri, money, key, presid, day, set, remain, issu, success, plan, edison, call, accord, last, sever, own, earli, spot, includ, year, appear, bid, base, balanc, get, staff, anoth, deliveri, sinc, got, control, allow, now, resourc, jeff, small, articl, charg, per, washington, dollar, better, seller, contract, benefit, board, larg, wednesday, help, near, sold, term, turn, construct, five, much, like, around, amount, direct, period, top, structur, offici, governor, signific, meet, rather, end, friday, report, may, fact, dont, agenc, offer, use, follow, file, taken, purpos, opinion, talk, certain, grow, good, keep, altern, busi, dasovichnaenron, exist, come, make, real, drive, avoid, pge, monday, effort, effici, month, back, repres, measur, davi, determin, exchang, develop, lead, need, januari, payment, caus, immedi, least, run, explain, messag, havent, next., tuesday, occur, san, receiv, program, upon, start, refer, ago, prohibit, approxim, although, differ, releas, legisl, analysi, just, later, open, chairman, region, move, clear, administr, forward, post, director, decemb, produc, believ, estim, alreadi, fix, found, recent, tri, consult, facil, particip, trader, seek, see, novemb, crisi, cpuc, richard, far, entir, investig, polici, retail, indic, free, execut, respons, materi, modifi, X100, citi, X2001, steven, new, third, cover, oblig, long, portion, august, suggest, basi, idea, return, possibl, tariff, procedur, might, person, regul, readi, condit, agre, though, due, four, combin, X1999, big, someth, comput, look, averag, replac, everi, member, press, tom, record, applic, focus, way, hold, particular, short, decis, becom, jame, connect, corp, posit, thing, coupl, howev, court, manag, potenti, littl, juli, thursday, made, put, detail, begin, answer, site, well, must, total, excess, effect, enter, involv, complet, chanc, togeth, negoti, therefor, advanc, notic, ensur, brian, except, home, morn, give, act, storag, accept, coordin, unless, confer, otherwis, privat, futur, publish, share, fail, web, inform, given, arrang, that, statement, project, soon, current, second, question, first, week, william, ask, commod, say, hope, schedul, intend, jim, reach, less, case, outsid, daili, section, instead, name, place, rule, guarante, address, govern, associ, practic, subject, lot, access, trade, locat, susan, model, invest, main, build, depart, financ, note, left, identifi, internet, reflect, actual, express, bill, general, support, import, qualiti, confidenti, view, show, comment, peopl, eric, correct, work, similar, assum, fund, dasovich, late, sign, seem, seen, cash, option, tell, add, word, commerci, flow, sale, limit, attorney, previous, concern, submit, karen, approach, data, necessari, decid, various, recommend, abl, along, water, physic, claim, final, error, number, event, think, paul, alan, april, parti, exampl, els, group, anyon, dissemin, distribut, onlin, realli, south, delet, improv, sue, law, individu, oil, prepar, hear, leav, advis, head, standard, cynthia, joe, notifi, speak, thought, jan, tim, know, awar, strong, page, mean, sender, activ, organ, east, present, protect, corpor, west, kevin, enron, initi, singl, area, relat, kind, everyon, firm, electron, extend, assist, affect, octob, origin, specif, attent, septemb, deliv, chang, york, rob, hard, north, valu, termin, capit, offic, either, calcul, yet, ill, consider, side, approv, request, shall, asset, product, feel, let, carol, someon, march, probabl, sent, commit, appropri, matter, depend, research, chris, keannaenronenron, quick, contain, hand, gari, respect, steve, guy, date, deal, tomorrow, repli, latest, interest, extens, scott, financi, liquid, texa, local, intern, entiti, vinc, anyth, team, read, great, via, risk, impact, proceed, desk, ive, done, harri, llc, afternoon, mari, michael, cours, best, perform, link, delay, account, committe, opportun, privileg, process, find, sure, mail, greg, send, legal, recipi, london, transact, confirm, mention, summari, convers, prior, visit, respond, bit, yesterday, websit, format, review, paper, georg, peter, handl, kelli, item, employe, net, provis, februari, suit, frank, robert, attend, strategi, dave, email.1, street, join, dan, list, ken, met, appreci, draft, contact, status, bob, version, type, appli, sorri, mark, credit, understand, kaminskihouect, etc, rick, brief, communic, document, bank, sheet, phone, gerald, ena, copi, fyi, counsel, john, updat, jeffrey, roger, lesli, lisa, agreement, volum, letter, elizabeth, settlement, discuss, houston, global, info, transfer, pleas, america, europ, smith, stephani, memo, dear, regard, fax, form, counterparti, languag, mike, master, X77002, david, check, print, amend, eol, kate, attach, book, X1400, revis, thank
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 151.197 54.87
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9999897 0.6 0.9948187 0.8846154
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.9907184 0.9999577 0.5172793 0.9169435
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.6185567 0.8560311
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8070588 0.896564 0.5317839# User specified
# easier to exclude features
#model_id_pfx <- "";
# indep_vars_vctr <- setdiff(names(glb_trnent_df),
# union(union(glb_rsp_var, glb_exclude_vars_as_features),
# c("<feat1_name>", "<feat2_name>")))
# method <- ""
# easier to include features
#model_id_pfx <- ""; indep_vars_vctr <- c("<feat1_name>", "<feat1_name>"); method <- ""
# User specified bivariate models
# indep_vars_vctr_lst <- list()
# for (feat in setdiff(names(glb_trnent_df),
# union(glb_rsp_var, glb_exclude_vars_as_features)))
# indep_vars_vctr_lst[["feat"]] <- feat
# User specified combinatorial models
# indep_vars_vctr_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indep_vars_vctr_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# template for myfit_mdl
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# ret_lst <- myfit_mdl_fn(model_id=paste0(model_id_pfx, ""), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_trnent_df, OOB_df=glb_newent_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df,
# model_loss_mtrx=glb_model_metric_terms,
# model_summaryFunction=glb_model_metric_smmry,
# model_metric=glb_model_metric,
# model_metric_maximize=glb_model_metric_maximize)
# Simplify a model
# fit_df <- glb_trnent_df; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
# data=glb_trnent_df, #method="class",
# control=rpart.control(cp=0.12),
# parms=list(loss=glb_model_metric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)## model_id model_method
## 1 MFO.myMFO_classfr myMFO_classfr
## 2 Random.myrandom_classfr myrandom_classfr
## 3 Max.cor.Y.cv.0.rpart rpart
## 4 Max.cor.Y.cv.0.cp.0.rpart rpart
## 5 Max.cor.Y.rpart rpart
## 6 Max.cor.Y.glm glm
## 7 Interact.High.cor.y.glm glm
## 8 Low.cor.X.glm glm
## 9 Conditional.X.glm glm
## 10 Conditional.X.rpart rpart
## 11 Conditional.X.cp.0.rpart rpart
## 12 Conditional.X.rf rf
## 13 Conditional.X.no.rnorm.rf rf
## feats
## 1 .rnorm
## 2 .rnorm
## 3 price
## 4 price
## 5 price
## 6 price
## 7 price, price:southern, price:summer, price:X2000, price:order, price:public, price:million, price:jone, price:said, price:system, price:busi, price:gas, price:take, price:bid, price:trade, price:higher, price:face, price:littl, price:includ, price:lower, price:make, price:sinc, price:drive, price:inc, price:news, price:base, price:rais, price:dollar, price:trader, price:result, price:grow, price:real, price:avoid, price:percent, price:construct, price:also, price:much, price:tariff, price:allow, price:need, price:everi, price:get, price:month, price:inform, price:now, price:submit, price:privat, price:just, price:X2001, price:dasovich, price:member, price:long, price:materi, price:offici, price:otherwis, price:davi, price:south, price:use, price:law, price:home, price:thing, price:improv, price:juli, price:practic, price:intend, price:futur, price:seek, price:combin, price:though, price:put, price:york, price:complet, price:cynthia, price:say, price:court, price:realli, price:peopl, price:water, price:depart, price:late, price:data, price:risk, price:committe, price:limit, price:protect, price:firm, price:mean, price:let, price:global, price:smith
## 8 independ, suppli, emerg, summer, custom, transmiss, iso, jone, forc, gas, longer, low, increas, expect, problem, consum, pay, plant, higher, rate, cap, commiss, sourc, solut, three, among, right, addit, author, line, energi, bring, push, major, load, told, lower, public, pipelin, peak, supplier, without, electr, hour, interconnect, econom, two, implement, analyst, june, close, establish, want, within, purchas, announc, enough, servic, inc, grid, provid, regulatori, news, steffesnaenronenron, creat, propos, still, situat, profit, X2000, whether, transport, paid, fall, action, serv, avail, today, full, unit, face, pass, compani, requir, cut, point, step, andor, earlier, design, past, pacif, sell, mani, reserv, one, nation, consid, billion, percent, deregul, industri, money, key, presid, day, set, remain, issu, success, plan, edison, call, accord, last, sever, own, earli, appear, base, balanc, get, staff, anoth, deliveri, sinc, got, control, small, articl, per, washington, dollar, better, seller, benefit, board, larg, wednesday, help, near, sold, term, construct, five, like, around, amount, direct, top, structur, governor, signific, meet, end, friday, report, fact, dont, agenc, offer, follow, file, taken, purpos, opinion, talk, certain, grow, good, keep, altern, busi, dasovichnaenron, exist, come, make, real, pge, monday, effort, month, back, repres, measur, determin, exchang, develop, lead, need, januari, payment, immedi, least, run, explain, messag, havent, next., tuesday, occur, san, receiv, program, upon, start, refer, ago, approxim, differ, releas, legisl, analysi, just, later, open, chairman, region, move, clear, administr, forward, post, director, decemb, produc, believ, estim, alreadi, fix, found, recent, tri, consult, facil, trader, see, novemb, cpuc, richard, far, entir, investig, polici, retail, indic, free, execut, respons, modifi, X100, citi, X2001, steven, third, cover, oblig, long, portion, august, suggest, basi, idea, return, possibl, tariff, procedur, might, person, readi, condit, agre, though, due, four, combin, X1999, big, someth, comput, look, averag, replac, everi, member, press, record, applic, focus, hold, short, decis, becom, jame, connect, corp, posit, coupl, howev, court, manag, potenti, littl, thursday, made, put, detail, begin, answer, site, well, must, total, excess, effect, enter, involv, complet, chanc, togeth, negoti, therefor, advanc, notic, ensur, brian, except, morn, give, act, storag, accept, coordin, unless, confer, otherwis, publish, share, fail, web, given, arrang, that, statement, project, soon, current, second, question, first, week, william, ask, commod, say, hope, schedul, intend, jim, reach, less, case, outsid, daili, section, instead, name, place, rule, guarante, address, govern, associ, practic, subject, lot, access, trade, locat, susan, model, invest, main, depart, financ, note, left, identifi, internet, reflect, actual, express, support, import, qualiti, confidenti, view, show, comment, peopl, eric, correct, work, similar, assum, fund, dasovich, late, sign, seem, seen, cash, option, tell, add, word, commerci, flow, sale, attorney, previous, concern, submit, karen, approach, data, necessari, decid, various, recommend, abl, along, water, physic, claim, final, error, number, event, think, paul, alan, april, parti, exampl, els, group, anyon, dissemin, distribut, onlin, realli, south, delet, sue, law, individu, oil, prepar, hear, leav, advis, head, cynthia, joe, notifi, speak, thought, jan, tim, awar, strong, page, mean, sender, activ, organ, east, present, protect, corpor, west, kevin, enron, initi, singl, area, relat, kind, everyon, firm, electron, extend, assist, affect, octob, origin, specif, attent, septemb, deliv, chang, york, rob, hard, north, valu, termin, capit, offic, either, calcul, yet, ill, consider, side, approv, request, shall, asset, product, feel, let, carol, someon, march, probabl, sent, commit, appropri, matter, depend, research, chris, keannaenronenron, quick, contain, hand, gari, respect, steve, guy, date, deal, tomorrow, repli, latest, interest, extens, scott, financi, liquid, texa, local, intern, entiti, vinc, anyth, team, read, great, via, risk, impact, proceed, desk, ive, done, harri, llc, afternoon, mari, michael, cours, best, perform, link, delay, account, committe, opportun, privileg, process, find, sure, mail, greg, send, legal, recipi, london, transact, confirm, mention, summari, convers, prior, visit, respond, bit, yesterday, websit, format, review, paper, georg, peter, handl, kelli, item, employe, net, provis, februari, suit, frank, robert, attend, strategi, dave, email.1, street, join, dan, list, ken, met, appreci, draft, contact, status, bob, version, type, appli, sorri, mark, credit, understand, kaminskihouect, etc, rick, brief, communic, document, bank, sheet, phone, gerald, ena, copi, fyi, counsel, john, updat, jeffrey, roger, lesli, lisa, agreement, volum, letter, elizabeth, .rnorm, settlement, discuss, houston, global, info, transfer, pleas, america, smith, stephani, memo, dear, regard, fax, form, counterparti, languag, mike, master, X77002, david, check, print, amend, eol, kate, attach, book, revis, thank
## 9 price, independ, demand, generat, suppli, emerg, summer, ferc, southern, util, custom, dow, high, oper, california, transmiss, natur, power, iso, jone, forc, gas, longer, capac, low, increas, expect, problem, consum, pay, system, plant, take, higher, rate, feder, said, continu, cap, commiss, sourc, solut, three, among, right, addit, order, author, line, energi, bring, push, major, load, told, can, lower, public, pipelin, peak, cost, supplier, without, electr, state, copyright, hour, interconnect, econom, two, implement, analyst, june, million, level, close, establish, want, within, purchas, other, part, announc, enough, servic, market, inc, grid, provid, competit, reduc, regulatori, news, steffesnaenronenron, creat, propos, still, situat, profit, X2000, whether, wholesal, transport, paid, fall, action, serv, rais, fuel, technolog, avail, will, today, full, unit, face, pass, compani, requir, cut, point, step, even, andor, earlier, design, past, result, pacif, sell, mani, reason, also, reserv, one, nation, time, consid, billion, percent, deregul, buy, industri, money, key, presid, day, set, remain, issu, success, plan, edison, call, accord, last, sever, own, earli, spot, includ, year, appear, bid, base, balanc, get, staff, anoth, deliveri, sinc, got, control, allow, now, resourc, jeff, small, articl, charg, per, washington, dollar, better, seller, contract, benefit, board, larg, wednesday, help, near, sold, term, turn, construct, five, much, like, around, amount, direct, period, top, structur, offici, governor, signific, meet, rather, end, friday, report, may, fact, dont, agenc, offer, use, follow, file, taken, purpos, opinion, talk, certain, grow, good, keep, altern, busi, dasovichnaenron, exist, come, make, real, drive, avoid, pge, monday, effort, effici, month, back, repres, measur, davi, determin, exchang, develop, lead, need, januari, payment, caus, immedi, least, run, explain, messag, havent, next., tuesday, occur, san, receiv, program, upon, start, refer, ago, prohibit, approxim, although, differ, releas, legisl, analysi, just, later, open, chairman, region, move, clear, administr, forward, post, director, decemb, produc, believ, estim, alreadi, fix, found, recent, tri, consult, facil, particip, trader, seek, see, novemb, crisi, cpuc, richard, far, entir, investig, polici, retail, indic, free, execut, respons, materi, modifi, X100, citi, X2001, steven, new, third, cover, oblig, long, portion, august, suggest, basi, idea, return, possibl, tariff, procedur, might, person, regul, readi, condit, agre, though, due, four, combin, X1999, big, someth, comput, look, averag, replac, everi, member, press, tom, record, applic, focus, way, hold, particular, short, decis, becom, jame, connect, corp, posit, thing, coupl, howev, court, manag, potenti, littl, juli, thursday, made, put, detail, begin, answer, site, well, must, total, excess, effect, enter, involv, complet, chanc, togeth, negoti, therefor, advanc, notic, ensur, brian, except, home, morn, give, act, storag, accept, coordin, unless, confer, otherwis, privat, futur, publish, share, fail, web, inform, given, arrang, that, statement, project, soon, current, second, question, first, week, william, ask, commod, say, hope, schedul, intend, jim, reach, less, case, outsid, daili, section, instead, name, place, rule, guarante, address, govern, associ, practic, subject, lot, access, trade, locat, susan, model, invest, main, build, depart, financ, note, left, identifi, internet, reflect, actual, express, bill, general, support, import, qualiti, confidenti, view, show, comment, peopl, eric, correct, work, similar, assum, fund, dasovich, late, sign, seem, seen, cash, option, tell, add, word, commerci, flow, sale, limit, attorney, previous, concern, submit, karen, approach, data, necessari, decid, various, recommend, abl, along, water, physic, claim, final, error, number, event, think, paul, alan, april, parti, exampl, els, group, anyon, dissemin, distribut, onlin, realli, south, delet, improv, sue, law, individu, oil, prepar, hear, leav, advis, head, standard, cynthia, joe, notifi, speak, thought, jan, tim, know, awar, strong, page, mean, sender, activ, organ, east, present, protect, corpor, west, kevin, enron, initi, singl, area, relat, kind, everyon, firm, electron, extend, assist, affect, octob, origin, specif, attent, septemb, deliv, chang, york, rob, hard, north, valu, termin, capit, offic, either, calcul, yet, ill, consider, side, approv, request, shall, asset, product, feel, let, carol, someon, march, probabl, sent, commit, appropri, matter, depend, research, chris, keannaenronenron, quick, contain, hand, gari, respect, steve, guy, date, deal, tomorrow, repli, latest, interest, extens, scott, financi, liquid, texa, local, intern, entiti, vinc, anyth, team, read, great, via, risk, impact, proceed, desk, ive, done, harri, llc, afternoon, mari, michael, cours, best, perform, link, delay, account, committe, opportun, privileg, process, find, sure, mail, greg, send, legal, recipi, london, transact, confirm, mention, summari, convers, prior, visit, respond, bit, yesterday, websit, format, review, paper, georg, peter, handl, kelli, item, employe, net, provis, februari, suit, frank, robert, attend, strategi, dave, email.1, street, join, dan, list, ken, met, appreci, draft, contact, status, bob, version, type, appli, sorri, mark, credit, understand, kaminskihouect, etc, rick, brief, communic, document, bank, sheet, phone, gerald, ena, copi, fyi, counsel, john, updat, jeffrey, roger, lesli, lisa, agreement, volum, letter, elizabeth, .rnorm, settlement, discuss, houston, global, info, transfer, pleas, america, europ, smith, stephani, memo, dear, regard, fax, form, counterparti, languag, mike, master, X77002, david, check, print, amend, eol, kate, attach, book, X1400, revis, thank
## 10 price, independ, demand, generat, suppli, emerg, summer, ferc, southern, util, custom, dow, high, oper, california, transmiss, natur, power, iso, jone, forc, gas, longer, capac, low, increas, expect, problem, consum, pay, system, plant, take, higher, rate, feder, said, continu, cap, commiss, sourc, solut, three, among, right, addit, order, author, line, energi, bring, push, major, load, told, can, lower, public, pipelin, peak, cost, supplier, without, electr, state, copyright, hour, interconnect, econom, two, implement, analyst, june, million, level, close, establish, want, within, purchas, other, part, announc, enough, servic, market, inc, grid, provid, competit, reduc, regulatori, news, steffesnaenronenron, creat, propos, still, situat, profit, X2000, whether, wholesal, transport, paid, fall, action, serv, rais, fuel, technolog, avail, will, today, full, unit, face, pass, compani, requir, cut, point, step, even, andor, earlier, design, past, result, pacif, sell, mani, reason, also, reserv, one, nation, time, consid, billion, percent, deregul, buy, industri, money, key, presid, day, set, remain, issu, success, plan, edison, call, accord, last, sever, own, earli, spot, includ, year, appear, bid, base, balanc, get, staff, anoth, deliveri, sinc, got, control, allow, now, resourc, jeff, small, articl, charg, per, washington, dollar, better, seller, contract, benefit, board, larg, wednesday, help, near, sold, term, turn, construct, five, much, like, around, amount, direct, period, top, structur, offici, governor, signific, meet, rather, end, friday, report, may, fact, dont, agenc, offer, use, follow, file, taken, purpos, opinion, talk, certain, grow, good, keep, altern, busi, dasovichnaenron, exist, come, make, real, drive, avoid, pge, monday, effort, effici, month, back, repres, measur, davi, determin, exchang, develop, lead, need, januari, payment, caus, immedi, least, run, explain, messag, havent, next., tuesday, occur, san, receiv, program, upon, start, refer, ago, prohibit, approxim, although, differ, releas, legisl, analysi, just, later, open, chairman, region, move, clear, administr, forward, post, director, decemb, produc, believ, estim, alreadi, fix, found, recent, tri, consult, facil, particip, trader, seek, see, novemb, crisi, cpuc, richard, far, entir, investig, polici, retail, indic, free, execut, respons, materi, modifi, X100, citi, X2001, steven, new, third, cover, oblig, long, portion, august, suggest, basi, idea, return, possibl, tariff, procedur, might, person, regul, readi, condit, agre, though, due, four, combin, X1999, big, someth, comput, look, averag, replac, everi, member, press, tom, record, applic, focus, way, hold, particular, short, decis, becom, jame, connect, corp, posit, thing, coupl, howev, court, manag, potenti, littl, juli, thursday, made, put, detail, begin, answer, site, well, must, total, excess, effect, enter, involv, complet, chanc, togeth, negoti, therefor, advanc, notic, ensur, brian, except, home, morn, give, act, storag, accept, coordin, unless, confer, otherwis, privat, futur, publish, share, fail, web, inform, given, arrang, that, statement, project, soon, current, second, question, first, week, william, ask, commod, say, hope, schedul, intend, jim, reach, less, case, outsid, daili, section, instead, name, place, rule, guarante, address, govern, associ, practic, subject, lot, access, trade, locat, susan, model, invest, main, build, depart, financ, note, left, identifi, internet, reflect, actual, express, bill, general, support, import, qualiti, confidenti, view, show, comment, peopl, eric, correct, work, similar, assum, fund, dasovich, late, sign, seem, seen, cash, option, tell, add, word, commerci, flow, sale, limit, attorney, previous, concern, submit, karen, approach, data, necessari, decid, various, recommend, abl, along, water, physic, claim, final, error, number, event, think, paul, alan, april, parti, exampl, els, group, anyon, dissemin, distribut, onlin, realli, south, delet, improv, sue, law, individu, oil, prepar, hear, leav, advis, head, standard, cynthia, joe, notifi, speak, thought, jan, tim, know, awar, strong, page, mean, sender, activ, organ, east, present, protect, corpor, west, kevin, enron, initi, singl, area, relat, kind, everyon, firm, electron, extend, assist, affect, octob, origin, specif, attent, septemb, deliv, chang, york, rob, hard, north, valu, termin, capit, offic, either, calcul, yet, ill, consider, side, approv, request, shall, asset, product, feel, let, carol, someon, march, probabl, sent, commit, appropri, matter, depend, research, chris, keannaenronenron, quick, contain, hand, gari, respect, steve, guy, date, deal, tomorrow, repli, latest, interest, extens, scott, financi, liquid, texa, local, intern, entiti, vinc, anyth, team, read, great, via, risk, impact, proceed, desk, ive, done, harri, llc, afternoon, mari, michael, cours, best, perform, link, delay, account, committe, opportun, privileg, process, find, sure, mail, greg, send, legal, recipi, london, transact, confirm, mention, summari, convers, prior, visit, respond, bit, yesterday, websit, format, review, paper, georg, peter, handl, kelli, item, employe, net, provis, februari, suit, frank, robert, attend, strategi, dave, email.1, street, join, dan, list, ken, met, appreci, draft, contact, status, bob, version, type, appli, sorri, mark, credit, understand, kaminskihouect, etc, rick, brief, communic, document, bank, sheet, phone, gerald, ena, copi, fyi, counsel, john, updat, jeffrey, roger, lesli, lisa, agreement, volum, letter, elizabeth, settlement, discuss, houston, global, info, transfer, pleas, america, europ, smith, stephani, memo, dear, regard, fax, form, counterparti, languag, mike, master, X77002, david, check, print, amend, eol, kate, attach, book, X1400, revis, thank
## 11 price, independ, demand, generat, suppli, emerg, summer, ferc, southern, util, custom, dow, high, oper, california, transmiss, natur, power, iso, jone, forc, gas, longer, capac, low, increas, expect, problem, consum, pay, system, plant, take, higher, rate, feder, said, continu, cap, commiss, sourc, solut, three, among, right, addit, order, author, line, energi, bring, push, major, load, told, can, lower, public, pipelin, peak, cost, supplier, without, electr, state, copyright, hour, interconnect, econom, two, implement, analyst, june, million, level, close, establish, want, within, purchas, other, part, announc, enough, servic, market, inc, grid, provid, competit, reduc, regulatori, news, steffesnaenronenron, creat, propos, still, situat, profit, X2000, whether, wholesal, transport, paid, fall, action, serv, rais, fuel, technolog, avail, will, today, full, unit, face, pass, compani, requir, cut, point, step, even, andor, earlier, design, past, result, pacif, sell, mani, reason, also, reserv, one, nation, time, consid, billion, percent, deregul, buy, industri, money, key, presid, day, set, remain, issu, success, plan, edison, call, accord, last, sever, own, earli, spot, includ, year, appear, bid, base, balanc, get, staff, anoth, deliveri, sinc, got, control, allow, now, resourc, jeff, small, articl, charg, per, washington, dollar, better, seller, contract, benefit, board, larg, wednesday, help, near, sold, term, turn, construct, five, much, like, around, amount, direct, period, top, structur, offici, governor, signific, meet, rather, end, friday, report, may, fact, dont, agenc, offer, use, follow, file, taken, purpos, opinion, talk, certain, grow, good, keep, altern, busi, dasovichnaenron, exist, come, make, real, drive, avoid, pge, monday, effort, effici, month, back, repres, measur, davi, determin, exchang, develop, lead, need, januari, payment, caus, immedi, least, run, explain, messag, havent, next., tuesday, occur, san, receiv, program, upon, start, refer, ago, prohibit, approxim, although, differ, releas, legisl, analysi, just, later, open, chairman, region, move, clear, administr, forward, post, director, decemb, produc, believ, estim, alreadi, fix, found, recent, tri, consult, facil, particip, trader, seek, see, novemb, crisi, cpuc, richard, far, entir, investig, polici, retail, indic, free, execut, respons, materi, modifi, X100, citi, X2001, steven, new, third, cover, oblig, long, portion, august, suggest, basi, idea, return, possibl, tariff, procedur, might, person, regul, readi, condit, agre, though, due, four, combin, X1999, big, someth, comput, look, averag, replac, everi, member, press, tom, record, applic, focus, way, hold, particular, short, decis, becom, jame, connect, corp, posit, thing, coupl, howev, court, manag, potenti, littl, juli, thursday, made, put, detail, begin, answer, site, well, must, total, excess, effect, enter, involv, complet, chanc, togeth, negoti, therefor, advanc, notic, ensur, brian, except, home, morn, give, act, storag, accept, coordin, unless, confer, otherwis, privat, futur, publish, share, fail, web, inform, given, arrang, that, statement, project, soon, current, second, question, first, week, william, ask, commod, say, hope, schedul, intend, jim, reach, less, case, outsid, daili, section, instead, name, place, rule, guarante, address, govern, associ, practic, subject, lot, access, trade, locat, susan, model, invest, main, build, depart, financ, note, left, identifi, internet, reflect, actual, express, bill, general, support, import, qualiti, confidenti, view, show, comment, peopl, eric, correct, work, similar, assum, fund, dasovich, late, sign, seem, seen, cash, option, tell, add, word, commerci, flow, sale, limit, attorney, previous, concern, submit, karen, approach, data, necessari, decid, various, recommend, abl, along, water, physic, claim, final, error, number, event, think, paul, alan, april, parti, exampl, els, group, anyon, dissemin, distribut, onlin, realli, south, delet, improv, sue, law, individu, oil, prepar, hear, leav, advis, head, standard, cynthia, joe, notifi, speak, thought, jan, tim, know, awar, strong, page, mean, sender, activ, organ, east, present, protect, corpor, west, kevin, enron, initi, singl, area, relat, kind, everyon, firm, electron, extend, assist, affect, octob, origin, specif, attent, septemb, deliv, chang, york, rob, hard, north, valu, termin, capit, offic, either, calcul, yet, ill, consider, side, approv, request, shall, asset, product, feel, let, carol, someon, march, probabl, sent, commit, appropri, matter, depend, research, chris, keannaenronenron, quick, contain, hand, gari, respect, steve, guy, date, deal, tomorrow, repli, latest, interest, extens, scott, financi, liquid, texa, local, intern, entiti, vinc, anyth, team, read, great, via, risk, impact, proceed, desk, ive, done, harri, llc, afternoon, mari, michael, cours, best, perform, link, delay, account, committe, opportun, privileg, process, find, sure, mail, greg, send, legal, recipi, london, transact, confirm, mention, summari, convers, prior, visit, respond, bit, yesterday, websit, format, review, paper, georg, peter, handl, kelli, item, employe, net, provis, februari, suit, frank, robert, attend, strategi, dave, email.1, street, join, dan, list, ken, met, appreci, draft, contact, status, bob, version, type, appli, sorri, mark, credit, understand, kaminskihouect, etc, rick, brief, communic, document, bank, sheet, phone, gerald, ena, copi, fyi, counsel, john, updat, jeffrey, roger, lesli, lisa, agreement, volum, letter, elizabeth, settlement, discuss, houston, global, info, transfer, pleas, america, europ, smith, stephani, memo, dear, regard, fax, form, counterparti, languag, mike, master, X77002, david, check, print, amend, eol, kate, attach, book, X1400, revis, thank
## 12 price, independ, demand, generat, suppli, emerg, summer, ferc, southern, util, custom, dow, high, oper, california, transmiss, natur, power, iso, jone, forc, gas, longer, capac, low, increas, expect, problem, consum, pay, system, plant, take, higher, rate, feder, said, continu, cap, commiss, sourc, solut, three, among, right, addit, order, author, line, energi, bring, push, major, load, told, can, lower, public, pipelin, peak, cost, supplier, without, electr, state, copyright, hour, interconnect, econom, two, implement, analyst, june, million, level, close, establish, want, within, purchas, other, part, announc, enough, servic, market, inc, grid, provid, competit, reduc, regulatori, news, steffesnaenronenron, creat, propos, still, situat, profit, X2000, whether, wholesal, transport, paid, fall, action, serv, rais, fuel, technolog, avail, will, today, full, unit, face, pass, compani, requir, cut, point, step, even, andor, earlier, design, past, result, pacif, sell, mani, reason, also, reserv, one, nation, time, consid, billion, percent, deregul, buy, industri, money, key, presid, day, set, remain, issu, success, plan, edison, call, accord, last, sever, own, earli, spot, includ, year, appear, bid, base, balanc, get, staff, anoth, deliveri, sinc, got, control, allow, now, resourc, jeff, small, articl, charg, per, washington, dollar, better, seller, contract, benefit, board, larg, wednesday, help, near, sold, term, turn, construct, five, much, like, around, amount, direct, period, top, structur, offici, governor, signific, meet, rather, end, friday, report, may, fact, dont, agenc, offer, use, follow, file, taken, purpos, opinion, talk, certain, grow, good, keep, altern, busi, dasovichnaenron, exist, come, make, real, drive, avoid, pge, monday, effort, effici, month, back, repres, measur, davi, determin, exchang, develop, lead, need, januari, payment, caus, immedi, least, run, explain, messag, havent, next., tuesday, occur, san, receiv, program, upon, start, refer, ago, prohibit, approxim, although, differ, releas, legisl, analysi, just, later, open, chairman, region, move, clear, administr, forward, post, director, decemb, produc, believ, estim, alreadi, fix, found, recent, tri, consult, facil, particip, trader, seek, see, novemb, crisi, cpuc, richard, far, entir, investig, polici, retail, indic, free, execut, respons, materi, modifi, X100, citi, X2001, steven, new, third, cover, oblig, long, portion, august, suggest, basi, idea, return, possibl, tariff, procedur, might, person, regul, readi, condit, agre, though, due, four, combin, X1999, big, someth, comput, look, averag, replac, everi, member, press, tom, record, applic, focus, way, hold, particular, short, decis, becom, jame, connect, corp, posit, thing, coupl, howev, court, manag, potenti, littl, juli, thursday, made, put, detail, begin, answer, site, well, must, total, excess, effect, enter, involv, complet, chanc, togeth, negoti, therefor, advanc, notic, ensur, brian, except, home, morn, give, act, storag, accept, coordin, unless, confer, otherwis, privat, futur, publish, share, fail, web, inform, given, arrang, that, statement, project, soon, current, second, question, first, week, william, ask, commod, say, hope, schedul, intend, jim, reach, less, case, outsid, daili, section, instead, name, place, rule, guarante, address, govern, associ, practic, subject, lot, access, trade, locat, susan, model, invest, main, build, depart, financ, note, left, identifi, internet, reflect, actual, express, bill, general, support, import, qualiti, confidenti, view, show, comment, peopl, eric, correct, work, similar, assum, fund, dasovich, late, sign, seem, seen, cash, option, tell, add, word, commerci, flow, sale, limit, attorney, previous, concern, submit, karen, approach, data, necessari, decid, various, recommend, abl, along, water, physic, claim, final, error, number, event, think, paul, alan, april, parti, exampl, els, group, anyon, dissemin, distribut, onlin, realli, south, delet, improv, sue, law, individu, oil, prepar, hear, leav, advis, head, standard, cynthia, joe, notifi, speak, thought, jan, tim, know, awar, strong, page, mean, sender, activ, organ, east, present, protect, corpor, west, kevin, enron, initi, singl, area, relat, kind, everyon, firm, electron, extend, assist, affect, octob, origin, specif, attent, septemb, deliv, chang, york, rob, hard, north, valu, termin, capit, offic, either, calcul, yet, ill, consider, side, approv, request, shall, asset, product, feel, let, carol, someon, march, probabl, sent, commit, appropri, matter, depend, research, chris, keannaenronenron, quick, contain, hand, gari, respect, steve, guy, date, deal, tomorrow, repli, latest, interest, extens, scott, financi, liquid, texa, local, intern, entiti, vinc, anyth, team, read, great, via, risk, impact, proceed, desk, ive, done, harri, llc, afternoon, mari, michael, cours, best, perform, link, delay, account, committe, opportun, privileg, process, find, sure, mail, greg, send, legal, recipi, london, transact, confirm, mention, summari, convers, prior, visit, respond, bit, yesterday, websit, format, review, paper, georg, peter, handl, kelli, item, employe, net, provis, februari, suit, frank, robert, attend, strategi, dave, email.1, street, join, dan, list, ken, met, appreci, draft, contact, status, bob, version, type, appli, sorri, mark, credit, understand, kaminskihouect, etc, rick, brief, communic, document, bank, sheet, phone, gerald, ena, copi, fyi, counsel, john, updat, jeffrey, roger, lesli, lisa, agreement, volum, letter, elizabeth, .rnorm, settlement, discuss, houston, global, info, transfer, pleas, america, europ, smith, stephani, memo, dear, regard, fax, form, counterparti, languag, mike, master, X77002, david, check, print, amend, eol, kate, attach, book, X1400, revis, thank
## 13 price, independ, demand, generat, suppli, emerg, summer, ferc, southern, util, custom, dow, high, oper, california, transmiss, natur, power, iso, jone, forc, gas, longer, capac, low, increas, expect, problem, consum, pay, system, plant, take, higher, rate, feder, said, continu, cap, commiss, sourc, solut, three, among, right, addit, order, author, line, energi, bring, push, major, load, told, can, lower, public, pipelin, peak, cost, supplier, without, electr, state, copyright, hour, interconnect, econom, two, implement, analyst, june, million, level, close, establish, want, within, purchas, other, part, announc, enough, servic, market, inc, grid, provid, competit, reduc, regulatori, news, steffesnaenronenron, creat, propos, still, situat, profit, X2000, whether, wholesal, transport, paid, fall, action, serv, rais, fuel, technolog, avail, will, today, full, unit, face, pass, compani, requir, cut, point, step, even, andor, earlier, design, past, result, pacif, sell, mani, reason, also, reserv, one, nation, time, consid, billion, percent, deregul, buy, industri, money, key, presid, day, set, remain, issu, success, plan, edison, call, accord, last, sever, own, earli, spot, includ, year, appear, bid, base, balanc, get, staff, anoth, deliveri, sinc, got, control, allow, now, resourc, jeff, small, articl, charg, per, washington, dollar, better, seller, contract, benefit, board, larg, wednesday, help, near, sold, term, turn, construct, five, much, like, around, amount, direct, period, top, structur, offici, governor, signific, meet, rather, end, friday, report, may, fact, dont, agenc, offer, use, follow, file, taken, purpos, opinion, talk, certain, grow, good, keep, altern, busi, dasovichnaenron, exist, come, make, real, drive, avoid, pge, monday, effort, effici, month, back, repres, measur, davi, determin, exchang, develop, lead, need, januari, payment, caus, immedi, least, run, explain, messag, havent, next., tuesday, occur, san, receiv, program, upon, start, refer, ago, prohibit, approxim, although, differ, releas, legisl, analysi, just, later, open, chairman, region, move, clear, administr, forward, post, director, decemb, produc, believ, estim, alreadi, fix, found, recent, tri, consult, facil, particip, trader, seek, see, novemb, crisi, cpuc, richard, far, entir, investig, polici, retail, indic, free, execut, respons, materi, modifi, X100, citi, X2001, steven, new, third, cover, oblig, long, portion, august, suggest, basi, idea, return, possibl, tariff, procedur, might, person, regul, readi, condit, agre, though, due, four, combin, X1999, big, someth, comput, look, averag, replac, everi, member, press, tom, record, applic, focus, way, hold, particular, short, decis, becom, jame, connect, corp, posit, thing, coupl, howev, court, manag, potenti, littl, juli, thursday, made, put, detail, begin, answer, site, well, must, total, excess, effect, enter, involv, complet, chanc, togeth, negoti, therefor, advanc, notic, ensur, brian, except, home, morn, give, act, storag, accept, coordin, unless, confer, otherwis, privat, futur, publish, share, fail, web, inform, given, arrang, that, statement, project, soon, current, second, question, first, week, william, ask, commod, say, hope, schedul, intend, jim, reach, less, case, outsid, daili, section, instead, name, place, rule, guarante, address, govern, associ, practic, subject, lot, access, trade, locat, susan, model, invest, main, build, depart, financ, note, left, identifi, internet, reflect, actual, express, bill, general, support, import, qualiti, confidenti, view, show, comment, peopl, eric, correct, work, similar, assum, fund, dasovich, late, sign, seem, seen, cash, option, tell, add, word, commerci, flow, sale, limit, attorney, previous, concern, submit, karen, approach, data, necessari, decid, various, recommend, abl, along, water, physic, claim, final, error, number, event, think, paul, alan, april, parti, exampl, els, group, anyon, dissemin, distribut, onlin, realli, south, delet, improv, sue, law, individu, oil, prepar, hear, leav, advis, head, standard, cynthia, joe, notifi, speak, thought, jan, tim, know, awar, strong, page, mean, sender, activ, organ, east, present, protect, corpor, west, kevin, enron, initi, singl, area, relat, kind, everyon, firm, electron, extend, assist, affect, octob, origin, specif, attent, septemb, deliv, chang, york, rob, hard, north, valu, termin, capit, offic, either, calcul, yet, ill, consider, side, approv, request, shall, asset, product, feel, let, carol, someon, march, probabl, sent, commit, appropri, matter, depend, research, chris, keannaenronenron, quick, contain, hand, gari, respect, steve, guy, date, deal, tomorrow, repli, latest, interest, extens, scott, financi, liquid, texa, local, intern, entiti, vinc, anyth, team, read, great, via, risk, impact, proceed, desk, ive, done, harri, llc, afternoon, mari, michael, cours, best, perform, link, delay, account, committe, opportun, privileg, process, find, sure, mail, greg, send, legal, recipi, london, transact, confirm, mention, summari, convers, prior, visit, respond, bit, yesterday, websit, format, review, paper, georg, peter, handl, kelli, item, employe, net, provis, februari, suit, frank, robert, attend, strategi, dave, email.1, street, join, dan, list, ken, met, appreci, draft, contact, status, bob, version, type, appli, sorri, mark, credit, understand, kaminskihouect, etc, rick, brief, communic, document, bank, sheet, phone, gerald, ena, copi, fyi, counsel, john, updat, jeffrey, roger, lesli, lisa, agreement, volum, letter, elizabeth, settlement, discuss, houston, global, info, transfer, pleas, america, europ, smith, stephani, memo, dear, regard, fax, form, counterparti, languag, mike, master, X77002, david, check, print, amend, eol, kate, attach, book, X1400, revis, thank
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.476 0.002
## 2 0 0.297 0.002
## 3 0 0.642 0.014
## 4 0 0.544 0.012
## 5 3 1.021 0.012
## 6 1 0.905 0.013
## 7 1 1.695 0.227
## 8 1 19.197 6.526
## 9 1 20.175 5.759
## 10 3 7.650 1.136
## 11 0 1.638 1.145
## 12 3 166.977 58.827
## 13 3 151.197 54.870
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5000000 0.5 0.0000000 0.8377926
## 2 0.4898348 0.1 0.2791367 0.1622074
## 3 0.5000000 0.5 0.0000000 0.8377926
## 4 0.7284092 0.3 0.5568182 0.8695652
## 5 0.7284092 0.3 0.5568182 0.8779397
## 6 0.7433484 0.2 0.5568182 0.8679062
## 7 0.7670432 0.9 0.6933333 0.8361390
## 8 0.8493426 0.9 0.7411168 0.5050335
## 9 0.7590283 0.9 0.5204461 0.5452345
## 10 0.6952075 0.7 0.5405405 0.8812563
## 11 0.8629545 0.6 0.7282609 0.9163880
## 12 1.0000000 0.5 1.0000000 0.8879599
## 13 0.9999897 0.6 0.9948187 0.8846154
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8057562 0.8664468 0.00000000 0.5000000
## 2 0.1335532 0.1942438 0.00000000 0.4923588
## 3 0.8057562 0.8664468 0.00000000 0.5000000
## 4 0.8399036 0.8955142 0.48128378 0.7246401
## 5 0.8399036 0.8955142 0.45230238 0.7246401
## 6 0.8399036 0.8955142 0.33325458 0.8167774
## 7 0.8987265 0.9431349 0.24514952 0.6553156
## 8 0.8893904 0.9358425 0.02266820 0.4558693
## 9 0.7491135 0.8166122 0.05021386 0.4581949
## 10 0.8580773 0.9106084 0.46413751 0.7482281
## 11 0.8912524 0.9373064 0.67902613 0.7936323
## 12 0.9938503 1.0000000 0.52679816 0.9187708
## 13 0.9907184 0.9999577 0.51727930 0.9169435
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.0000000 0.8365759
## 2 0.1 0.2809365 0.1634241
## 3 0.5 0.0000000 0.8365759
## 4 0.3 0.5301205 0.8482490
## 5 0.3 0.5301205 0.8482490
## 6 0.2 0.5301205 0.8482490
## 7 0.9 0.4477612 0.8560311
## 8 0.0 0.2809365 0.1634241
## 9 0.0 0.2809365 0.1634241
## 10 0.7 0.6052632 0.8832685
## 11 0.7 0.6172840 0.8793774
## 12 0.4 0.6185567 0.8560311
## 13 0.4 0.6185567 0.8560311
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.7855868 0.8796081 0.0000000
## 2 0.1203919 0.2144132 0.0000000
## 3 0.7855868 0.8796081 0.0000000
## 4 0.7984412 0.8898113 0.4396489
## 5 0.7984412 0.8898113 0.4396489
## 6 0.7984412 0.8898113 0.4396489
## 7 0.8070588 0.8965640 0.3710563
## 8 0.1203919 0.2144132 0.0000000
## 9 0.1203919 0.2144132 0.0000000
## 10 0.8375671 0.9198400 0.5376589
## 11 0.8331724 0.9165525 0.5458070
## 12 0.8070588 0.8965640 0.5317839
## 13 0.8070588 0.8965640 0.5317839
## max.AccuracySD.fit max.KappaSD.fit min.aic.fit
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 0.007320661 0.04546369 NA
## 6 0.007293418 0.05256359 445.6427
## 7 0.009968522 0.04576649 3498.0161
## 8 0.024225071 0.05676480 4864.4526
## 9 0.043576853 0.05204949 10487.2626
## 10 0.023847226 0.13308732 NA
## 11 NA NA NA
## 12 NA NA NA
## 13 NA NA NAglb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="fit.models",
chunk_step_major=glb_script_df[nrow(glb_script_df), "chunk_step_major"],
chunk_step_minor=glb_script_df[nrow(glb_script_df), "chunk_step_minor"]+1,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))## chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed8 fit.models 5 0 474.244
## elapsed9 fit.models 5 1 965.105if (!is.null(glb_model_metric_smmry)) {
stats_df <- glb_models_df[, "model_id", FALSE]
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_trnent_df, glb_rsp_var,
glb_rsp_var_out, model_id, "fit",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_newent_df, glb_rsp_var,
glb_rsp_var_out, model_id, "OOB",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
# tmp_models_df <- orderBy(~model_id, glb_models_df)
# rownames(tmp_models_df) <- seq(1, nrow(tmp_models_df))
# all.equal(subset(tmp_models_df[, names(stats_df)], model_id != "Random.myrandom_classfr"),
# subset(stats_df, model_id != "Random.myrandom_classfr"))
# print(subset(tmp_models_df[, names(stats_df)], model_id != "Random.myrandom_classfr")[, c("model_id", "max.Accuracy.fit")])
# print(subset(stats_df, model_id != "Random.myrandom_classfr")[, c("model_id", "max.Accuracy.fit")])
print("Merging following data into glb_models_df:")
print(stats_mrg_df <- stats_df[, c(1, grep(glb_model_metric, names(stats_df)))])
print(tmp_models_df <- orderBy(~model_id, glb_models_df[, c("model_id", grep(glb_model_metric, names(stats_df), value=TRUE))]))
tmp2_models_df <- glb_models_df[, c("model_id", setdiff(names(glb_models_df), grep(glb_model_metric, names(stats_df), value=TRUE)))]
tmp3_models_df <- merge(tmp2_models_df, stats_mrg_df, all.x=TRUE, sort=FALSE)
print(tmp3_models_df)
print(names(tmp3_models_df))
print(glb_models_df <- subset(tmp3_models_df, select=-model_id.1))
}
plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)## model_id model_method
## 1 MFO.myMFO_classfr myMFO_classfr
## 2 Random.myrandom_classfr myrandom_classfr
## 3 Max.cor.Y.cv.0.rpart rpart
## 4 Max.cor.Y.cv.0.cp.0.rpart rpart
## 5 Max.cor.Y.rpart rpart
## 6 Max.cor.Y.glm glm
## 7 Interact.High.cor.y.glm glm
## 8 Low.cor.X.glm glm
## 9 Conditional.X.glm glm
## 10 Conditional.X.rpart rpart
## 11 Conditional.X.cp.0.rpart rpart
## 12 Conditional.X.rf rf
## 13 Conditional.X.no.rnorm.rf rf
## feats
## 1 .rnorm
## 2 .rnorm
## 3 price
## 4 price
## 5 price
## 6 price
## 7 price, price:southern, price:summer, price:X2000, price:order, price:public, price:million, price:jone, price:said, price:system, price:busi, price:gas, price:take, price:bid, price:trade, price:higher, price:face, price:littl, price:includ, price:lower, price:make, price:sinc, price:drive, price:inc, price:news, price:base, price:rais, price:dollar, price:trader, price:result, price:grow, price:real, price:avoid, price:percent, price:construct, price:also, price:much, price:tariff, price:allow, price:need, price:everi, price:get, price:month, price:inform, price:now, price:submit, price:privat, price:just, price:X2001, price:dasovich, price:member, price:long, price:materi, price:offici, price:otherwis, price:davi, price:south, price:use, price:law, price:home, price:thing, price:improv, price:juli, price:practic, price:intend, price:futur, price:seek, price:combin, price:though, price:put, price:york, price:complet, price:cynthia, price:say, price:court, price:realli, price:peopl, price:water, price:depart, price:late, price:data, price:risk, price:committe, price:limit, price:protect, price:firm, price:mean, price:let, price:global, price:smith
## 8 independ, suppli, emerg, summer, custom, transmiss, iso, jone, forc, gas, longer, low, increas, expect, problem, consum, pay, plant, higher, rate, cap, commiss, sourc, solut, three, among, right, addit, author, line, energi, bring, push, major, load, told, lower, public, pipelin, peak, supplier, without, electr, hour, interconnect, econom, two, implement, analyst, june, close, establish, want, within, purchas, announc, enough, servic, inc, grid, provid, regulatori, news, steffesnaenronenron, creat, propos, still, situat, profit, X2000, whether, transport, paid, fall, action, serv, avail, today, full, unit, face, pass, compani, requir, cut, point, step, andor, earlier, design, past, pacif, sell, mani, reserv, one, nation, consid, billion, percent, deregul, industri, money, key, presid, day, set, remain, issu, success, plan, edison, call, accord, last, sever, own, earli, appear, base, balanc, get, staff, anoth, deliveri, sinc, got, control, small, articl, per, washington, dollar, better, seller, benefit, board, larg, wednesday, help, near, sold, term, construct, five, like, around, amount, direct, top, structur, governor, signific, meet, end, friday, report, fact, dont, agenc, offer, follow, file, taken, purpos, opinion, talk, certain, grow, good, keep, altern, busi, dasovichnaenron, exist, come, make, real, pge, monday, effort, month, back, repres, measur, determin, exchang, develop, lead, need, januari, payment, immedi, least, run, explain, messag, havent, next., tuesday, occur, san, receiv, program, upon, start, refer, ago, approxim, differ, releas, legisl, analysi, just, later, open, chairman, region, move, clear, administr, forward, post, director, decemb, produc, believ, estim, alreadi, fix, found, recent, tri, consult, facil, trader, see, novemb, cpuc, richard, far, entir, investig, polici, retail, indic, free, execut, respons, modifi, X100, citi, X2001, steven, third, cover, oblig, long, portion, august, suggest, basi, idea, return, possibl, tariff, procedur, might, person, readi, condit, agre, though, due, four, combin, X1999, big, someth, comput, look, averag, replac, everi, member, press, record, applic, focus, hold, short, decis, becom, jame, connect, corp, posit, coupl, howev, court, manag, potenti, littl, thursday, made, put, detail, begin, answer, site, well, must, total, excess, effect, enter, involv, complet, chanc, togeth, negoti, therefor, advanc, notic, ensur, brian, except, morn, give, act, storag, accept, coordin, unless, confer, otherwis, publish, share, fail, web, given, arrang, that, statement, project, soon, current, second, question, first, week, william, ask, commod, say, hope, schedul, intend, jim, reach, less, case, outsid, daili, section, instead, name, place, rule, guarante, address, govern, associ, practic, subject, lot, access, trade, locat, susan, model, invest, main, depart, financ, note, left, identifi, internet, reflect, actual, express, support, import, qualiti, confidenti, view, show, comment, peopl, eric, correct, work, similar, assum, fund, dasovich, late, sign, seem, seen, cash, option, tell, add, word, commerci, flow, sale, attorney, previous, concern, submit, karen, approach, data, necessari, decid, various, recommend, abl, along, water, physic, claim, final, error, number, event, think, paul, alan, april, parti, exampl, els, group, anyon, dissemin, distribut, onlin, realli, south, delet, sue, law, individu, oil, prepar, hear, leav, advis, head, cynthia, joe, notifi, speak, thought, jan, tim, awar, strong, page, mean, sender, activ, organ, east, present, protect, corpor, west, kevin, enron, initi, singl, area, relat, kind, everyon, firm, electron, extend, assist, affect, octob, origin, specif, attent, septemb, deliv, chang, york, rob, hard, north, valu, termin, capit, offic, either, calcul, yet, ill, consider, side, approv, request, shall, asset, product, feel, let, carol, someon, march, probabl, sent, commit, appropri, matter, depend, research, chris, keannaenronenron, quick, contain, hand, gari, respect, steve, guy, date, deal, tomorrow, repli, latest, interest, extens, scott, financi, liquid, texa, local, intern, entiti, vinc, anyth, team, read, great, via, risk, impact, proceed, desk, ive, done, harri, llc, afternoon, mari, michael, cours, best, perform, link, delay, account, committe, opportun, privileg, process, find, sure, mail, greg, send, legal, recipi, london, transact, confirm, mention, summari, convers, prior, visit, respond, bit, yesterday, websit, format, review, paper, georg, peter, handl, kelli, item, employe, net, provis, februari, suit, frank, robert, attend, strategi, dave, email.1, street, join, dan, list, ken, met, appreci, draft, contact, status, bob, version, type, appli, sorri, mark, credit, understand, kaminskihouect, etc, rick, brief, communic, document, bank, sheet, phone, gerald, ena, copi, fyi, counsel, john, updat, jeffrey, roger, lesli, lisa, agreement, volum, letter, elizabeth, .rnorm, settlement, discuss, houston, global, info, transfer, pleas, america, smith, stephani, memo, dear, regard, fax, form, counterparti, languag, mike, master, X77002, david, check, print, amend, eol, kate, attach, book, revis, thank
## 9 price, independ, demand, generat, suppli, emerg, summer, ferc, southern, util, custom, dow, high, oper, california, transmiss, natur, power, iso, jone, forc, gas, longer, capac, low, increas, expect, problem, consum, pay, system, plant, take, higher, rate, feder, said, continu, cap, commiss, sourc, solut, three, among, right, addit, order, author, line, energi, bring, push, major, load, told, can, lower, public, pipelin, peak, cost, supplier, without, electr, state, copyright, hour, interconnect, econom, two, implement, analyst, june, million, level, close, establish, want, within, purchas, other, part, announc, enough, servic, market, inc, grid, provid, competit, reduc, regulatori, news, steffesnaenronenron, creat, propos, still, situat, profit, X2000, whether, wholesal, transport, paid, fall, action, serv, rais, fuel, technolog, avail, will, today, full, unit, face, pass, compani, requir, cut, point, step, even, andor, earlier, design, past, result, pacif, sell, mani, reason, also, reserv, one, nation, time, consid, billion, percent, deregul, buy, industri, money, key, presid, day, set, remain, issu, success, plan, edison, call, accord, last, sever, own, earli, spot, includ, year, appear, bid, base, balanc, get, staff, anoth, deliveri, sinc, got, control, allow, now, resourc, jeff, small, articl, charg, per, washington, dollar, better, seller, contract, benefit, board, larg, wednesday, help, near, sold, term, turn, construct, five, much, like, around, amount, direct, period, top, structur, offici, governor, signific, meet, rather, end, friday, report, may, fact, dont, agenc, offer, use, follow, file, taken, purpos, opinion, talk, certain, grow, good, keep, altern, busi, dasovichnaenron, exist, come, make, real, drive, avoid, pge, monday, effort, effici, month, back, repres, measur, davi, determin, exchang, develop, lead, need, januari, payment, caus, immedi, least, run, explain, messag, havent, next., tuesday, occur, san, receiv, program, upon, start, refer, ago, prohibit, approxim, although, differ, releas, legisl, analysi, just, later, open, chairman, region, move, clear, administr, forward, post, director, decemb, produc, believ, estim, alreadi, fix, found, recent, tri, consult, facil, particip, trader, seek, see, novemb, crisi, cpuc, richard, far, entir, investig, polici, retail, indic, free, execut, respons, materi, modifi, X100, citi, X2001, steven, new, third, cover, oblig, long, portion, august, suggest, basi, idea, return, possibl, tariff, procedur, might, person, regul, readi, condit, agre, though, due, four, combin, X1999, big, someth, comput, look, averag, replac, everi, member, press, tom, record, applic, focus, way, hold, particular, short, decis, becom, jame, connect, corp, posit, thing, coupl, howev, court, manag, potenti, littl, juli, thursday, made, put, detail, begin, answer, site, well, must, total, excess, effect, enter, involv, complet, chanc, togeth, negoti, therefor, advanc, notic, ensur, brian, except, home, morn, give, act, storag, accept, coordin, unless, confer, otherwis, privat, futur, publish, share, fail, web, inform, given, arrang, that, statement, project, soon, current, second, question, first, week, william, ask, commod, say, hope, schedul, intend, jim, reach, less, case, outsid, daili, section, instead, name, place, rule, guarante, address, govern, associ, practic, subject, lot, access, trade, locat, susan, model, invest, main, build, depart, financ, note, left, identifi, internet, reflect, actual, express, bill, general, support, import, qualiti, confidenti, view, show, comment, peopl, eric, correct, work, similar, assum, fund, dasovich, late, sign, seem, seen, cash, option, tell, add, word, commerci, flow, sale, limit, attorney, previous, concern, submit, karen, approach, data, necessari, decid, various, recommend, abl, along, water, physic, claim, final, error, number, event, think, paul, alan, april, parti, exampl, els, group, anyon, dissemin, distribut, onlin, realli, south, delet, improv, sue, law, individu, oil, prepar, hear, leav, advis, head, standard, cynthia, joe, notifi, speak, thought, jan, tim, know, awar, strong, page, mean, sender, activ, organ, east, present, protect, corpor, west, kevin, enron, initi, singl, area, relat, kind, everyon, firm, electron, extend, assist, affect, octob, origin, specif, attent, septemb, deliv, chang, york, rob, hard, north, valu, termin, capit, offic, either, calcul, yet, ill, consider, side, approv, request, shall, asset, product, feel, let, carol, someon, march, probabl, sent, commit, appropri, matter, depend, research, chris, keannaenronenron, quick, contain, hand, gari, respect, steve, guy, date, deal, tomorrow, repli, latest, interest, extens, scott, financi, liquid, texa, local, intern, entiti, vinc, anyth, team, read, great, via, risk, impact, proceed, desk, ive, done, harri, llc, afternoon, mari, michael, cours, best, perform, link, delay, account, committe, opportun, privileg, process, find, sure, mail, greg, send, legal, recipi, london, transact, confirm, mention, summari, convers, prior, visit, respond, bit, yesterday, websit, format, review, paper, georg, peter, handl, kelli, item, employe, net, provis, februari, suit, frank, robert, attend, strategi, dave, email.1, street, join, dan, list, ken, met, appreci, draft, contact, status, bob, version, type, appli, sorri, mark, credit, understand, kaminskihouect, etc, rick, brief, communic, document, bank, sheet, phone, gerald, ena, copi, fyi, counsel, john, updat, jeffrey, roger, lesli, lisa, agreement, volum, letter, elizabeth, .rnorm, settlement, discuss, houston, global, info, transfer, pleas, america, europ, smith, stephani, memo, dear, regard, fax, form, counterparti, languag, mike, master, X77002, david, check, print, amend, eol, kate, attach, book, X1400, revis, thank
## 10 price, independ, demand, generat, suppli, emerg, summer, ferc, southern, util, custom, dow, high, oper, california, transmiss, natur, power, iso, jone, forc, gas, longer, capac, low, increas, expect, problem, consum, pay, system, plant, take, higher, rate, feder, said, continu, cap, commiss, sourc, solut, three, among, right, addit, order, author, line, energi, bring, push, major, load, told, can, lower, public, pipelin, peak, cost, supplier, without, electr, state, copyright, hour, interconnect, econom, two, implement, analyst, june, million, level, close, establish, want, within, purchas, other, part, announc, enough, servic, market, inc, grid, provid, competit, reduc, regulatori, news, steffesnaenronenron, creat, propos, still, situat, profit, X2000, whether, wholesal, transport, paid, fall, action, serv, rais, fuel, technolog, avail, will, today, full, unit, face, pass, compani, requir, cut, point, step, even, andor, earlier, design, past, result, pacif, sell, mani, reason, also, reserv, one, nation, time, consid, billion, percent, deregul, buy, industri, money, key, presid, day, set, remain, issu, success, plan, edison, call, accord, last, sever, own, earli, spot, includ, year, appear, bid, base, balanc, get, staff, anoth, deliveri, sinc, got, control, allow, now, resourc, jeff, small, articl, charg, per, washington, dollar, better, seller, contract, benefit, board, larg, wednesday, help, near, sold, term, turn, construct, five, much, like, around, amount, direct, period, top, structur, offici, governor, signific, meet, rather, end, friday, report, may, fact, dont, agenc, offer, use, follow, file, taken, purpos, opinion, talk, certain, grow, good, keep, altern, busi, dasovichnaenron, exist, come, make, real, drive, avoid, pge, monday, effort, effici, month, back, repres, measur, davi, determin, exchang, develop, lead, need, januari, payment, caus, immedi, least, run, explain, messag, havent, next., tuesday, occur, san, receiv, program, upon, start, refer, ago, prohibit, approxim, although, differ, releas, legisl, analysi, just, later, open, chairman, region, move, clear, administr, forward, post, director, decemb, produc, believ, estim, alreadi, fix, found, recent, tri, consult, facil, particip, trader, seek, see, novemb, crisi, cpuc, richard, far, entir, investig, polici, retail, indic, free, execut, respons, materi, modifi, X100, citi, X2001, steven, new, third, cover, oblig, long, portion, august, suggest, basi, idea, return, possibl, tariff, procedur, might, person, regul, readi, condit, agre, though, due, four, combin, X1999, big, someth, comput, look, averag, replac, everi, member, press, tom, record, applic, focus, way, hold, particular, short, decis, becom, jame, connect, corp, posit, thing, coupl, howev, court, manag, potenti, littl, juli, thursday, made, put, detail, begin, answer, site, well, must, total, excess, effect, enter, involv, complet, chanc, togeth, negoti, therefor, advanc, notic, ensur, brian, except, home, morn, give, act, storag, accept, coordin, unless, confer, otherwis, privat, futur, publish, share, fail, web, inform, given, arrang, that, statement, project, soon, current, second, question, first, week, william, ask, commod, say, hope, schedul, intend, jim, reach, less, case, outsid, daili, section, instead, name, place, rule, guarante, address, govern, associ, practic, subject, lot, access, trade, locat, susan, model, invest, main, build, depart, financ, note, left, identifi, internet, reflect, actual, express, bill, general, support, import, qualiti, confidenti, view, show, comment, peopl, eric, correct, work, similar, assum, fund, dasovich, late, sign, seem, seen, cash, option, tell, add, word, commerci, flow, sale, limit, attorney, previous, concern, submit, karen, approach, data, necessari, decid, various, recommend, abl, along, water, physic, claim, final, error, number, event, think, paul, alan, april, parti, exampl, els, group, anyon, dissemin, distribut, onlin, realli, south, delet, improv, sue, law, individu, oil, prepar, hear, leav, advis, head, standard, cynthia, joe, notifi, speak, thought, jan, tim, know, awar, strong, page, mean, sender, activ, organ, east, present, protect, corpor, west, kevin, enron, initi, singl, area, relat, kind, everyon, firm, electron, extend, assist, affect, octob, origin, specif, attent, septemb, deliv, chang, york, rob, hard, north, valu, termin, capit, offic, either, calcul, yet, ill, consider, side, approv, request, shall, asset, product, feel, let, carol, someon, march, probabl, sent, commit, appropri, matter, depend, research, chris, keannaenronenron, quick, contain, hand, gari, respect, steve, guy, date, deal, tomorrow, repli, latest, interest, extens, scott, financi, liquid, texa, local, intern, entiti, vinc, anyth, team, read, great, via, risk, impact, proceed, desk, ive, done, harri, llc, afternoon, mari, michael, cours, best, perform, link, delay, account, committe, opportun, privileg, process, find, sure, mail, greg, send, legal, recipi, london, transact, confirm, mention, summari, convers, prior, visit, respond, bit, yesterday, websit, format, review, paper, georg, peter, handl, kelli, item, employe, net, provis, februari, suit, frank, robert, attend, strategi, dave, email.1, street, join, dan, list, ken, met, appreci, draft, contact, status, bob, version, type, appli, sorri, mark, credit, understand, kaminskihouect, etc, rick, brief, communic, document, bank, sheet, phone, gerald, ena, copi, fyi, counsel, john, updat, jeffrey, roger, lesli, lisa, agreement, volum, letter, elizabeth, settlement, discuss, houston, global, info, transfer, pleas, america, europ, smith, stephani, memo, dear, regard, fax, form, counterparti, languag, mike, master, X77002, david, check, print, amend, eol, kate, attach, book, X1400, revis, thank
## 11 price, independ, demand, generat, suppli, emerg, summer, ferc, southern, util, custom, dow, high, oper, california, transmiss, natur, power, iso, jone, forc, gas, longer, capac, low, increas, expect, problem, consum, pay, system, plant, take, higher, rate, feder, said, continu, cap, commiss, sourc, solut, three, among, right, addit, order, author, line, energi, bring, push, major, load, told, can, lower, public, pipelin, peak, cost, supplier, without, electr, state, copyright, hour, interconnect, econom, two, implement, analyst, june, million, level, close, establish, want, within, purchas, other, part, announc, enough, servic, market, inc, grid, provid, competit, reduc, regulatori, news, steffesnaenronenron, creat, propos, still, situat, profit, X2000, whether, wholesal, transport, paid, fall, action, serv, rais, fuel, technolog, avail, will, today, full, unit, face, pass, compani, requir, cut, point, step, even, andor, earlier, design, past, result, pacif, sell, mani, reason, also, reserv, one, nation, time, consid, billion, percent, deregul, buy, industri, money, key, presid, day, set, remain, issu, success, plan, edison, call, accord, last, sever, own, earli, spot, includ, year, appear, bid, base, balanc, get, staff, anoth, deliveri, sinc, got, control, allow, now, resourc, jeff, small, articl, charg, per, washington, dollar, better, seller, contract, benefit, board, larg, wednesday, help, near, sold, term, turn, construct, five, much, like, around, amount, direct, period, top, structur, offici, governor, signific, meet, rather, end, friday, report, may, fact, dont, agenc, offer, use, follow, file, taken, purpos, opinion, talk, certain, grow, good, keep, altern, busi, dasovichnaenron, exist, come, make, real, drive, avoid, pge, monday, effort, effici, month, back, repres, measur, davi, determin, exchang, develop, lead, need, januari, payment, caus, immedi, least, run, explain, messag, havent, next., tuesday, occur, san, receiv, program, upon, start, refer, ago, prohibit, approxim, although, differ, releas, legisl, analysi, just, later, open, chairman, region, move, clear, administr, forward, post, director, decemb, produc, believ, estim, alreadi, fix, found, recent, tri, consult, facil, particip, trader, seek, see, novemb, crisi, cpuc, richard, far, entir, investig, polici, retail, indic, free, execut, respons, materi, modifi, X100, citi, X2001, steven, new, third, cover, oblig, long, portion, august, suggest, basi, idea, return, possibl, tariff, procedur, might, person, regul, readi, condit, agre, though, due, four, combin, X1999, big, someth, comput, look, averag, replac, everi, member, press, tom, record, applic, focus, way, hold, particular, short, decis, becom, jame, connect, corp, posit, thing, coupl, howev, court, manag, potenti, littl, juli, thursday, made, put, detail, begin, answer, site, well, must, total, excess, effect, enter, involv, complet, chanc, togeth, negoti, therefor, advanc, notic, ensur, brian, except, home, morn, give, act, storag, accept, coordin, unless, confer, otherwis, privat, futur, publish, share, fail, web, inform, given, arrang, that, statement, project, soon, current, second, question, first, week, william, ask, commod, say, hope, schedul, intend, jim, reach, less, case, outsid, daili, section, instead, name, place, rule, guarante, address, govern, associ, practic, subject, lot, access, trade, locat, susan, model, invest, main, build, depart, financ, note, left, identifi, internet, reflect, actual, express, bill, general, support, import, qualiti, confidenti, view, show, comment, peopl, eric, correct, work, similar, assum, fund, dasovich, late, sign, seem, seen, cash, option, tell, add, word, commerci, flow, sale, limit, attorney, previous, concern, submit, karen, approach, data, necessari, decid, various, recommend, abl, along, water, physic, claim, final, error, number, event, think, paul, alan, april, parti, exampl, els, group, anyon, dissemin, distribut, onlin, realli, south, delet, improv, sue, law, individu, oil, prepar, hear, leav, advis, head, standard, cynthia, joe, notifi, speak, thought, jan, tim, know, awar, strong, page, mean, sender, activ, organ, east, present, protect, corpor, west, kevin, enron, initi, singl, area, relat, kind, everyon, firm, electron, extend, assist, affect, octob, origin, specif, attent, septemb, deliv, chang, york, rob, hard, north, valu, termin, capit, offic, either, calcul, yet, ill, consider, side, approv, request, shall, asset, product, feel, let, carol, someon, march, probabl, sent, commit, appropri, matter, depend, research, chris, keannaenronenron, quick, contain, hand, gari, respect, steve, guy, date, deal, tomorrow, repli, latest, interest, extens, scott, financi, liquid, texa, local, intern, entiti, vinc, anyth, team, read, great, via, risk, impact, proceed, desk, ive, done, harri, llc, afternoon, mari, michael, cours, best, perform, link, delay, account, committe, opportun, privileg, process, find, sure, mail, greg, send, legal, recipi, london, transact, confirm, mention, summari, convers, prior, visit, respond, bit, yesterday, websit, format, review, paper, georg, peter, handl, kelli, item, employe, net, provis, februari, suit, frank, robert, attend, strategi, dave, email.1, street, join, dan, list, ken, met, appreci, draft, contact, status, bob, version, type, appli, sorri, mark, credit, understand, kaminskihouect, etc, rick, brief, communic, document, bank, sheet, phone, gerald, ena, copi, fyi, counsel, john, updat, jeffrey, roger, lesli, lisa, agreement, volum, letter, elizabeth, settlement, discuss, houston, global, info, transfer, pleas, america, europ, smith, stephani, memo, dear, regard, fax, form, counterparti, languag, mike, master, X77002, david, check, print, amend, eol, kate, attach, book, X1400, revis, thank
## 12 price, independ, demand, generat, suppli, emerg, summer, ferc, southern, util, custom, dow, high, oper, california, transmiss, natur, power, iso, jone, forc, gas, longer, capac, low, increas, expect, problem, consum, pay, system, plant, take, higher, rate, feder, said, continu, cap, commiss, sourc, solut, three, among, right, addit, order, author, line, energi, bring, push, major, load, told, can, lower, public, pipelin, peak, cost, supplier, without, electr, state, copyright, hour, interconnect, econom, two, implement, analyst, june, million, level, close, establish, want, within, purchas, other, part, announc, enough, servic, market, inc, grid, provid, competit, reduc, regulatori, news, steffesnaenronenron, creat, propos, still, situat, profit, X2000, whether, wholesal, transport, paid, fall, action, serv, rais, fuel, technolog, avail, will, today, full, unit, face, pass, compani, requir, cut, point, step, even, andor, earlier, design, past, result, pacif, sell, mani, reason, also, reserv, one, nation, time, consid, billion, percent, deregul, buy, industri, money, key, presid, day, set, remain, issu, success, plan, edison, call, accord, last, sever, own, earli, spot, includ, year, appear, bid, base, balanc, get, staff, anoth, deliveri, sinc, got, control, allow, now, resourc, jeff, small, articl, charg, per, washington, dollar, better, seller, contract, benefit, board, larg, wednesday, help, near, sold, term, turn, construct, five, much, like, around, amount, direct, period, top, structur, offici, governor, signific, meet, rather, end, friday, report, may, fact, dont, agenc, offer, use, follow, file, taken, purpos, opinion, talk, certain, grow, good, keep, altern, busi, dasovichnaenron, exist, come, make, real, drive, avoid, pge, monday, effort, effici, month, back, repres, measur, davi, determin, exchang, develop, lead, need, januari, payment, caus, immedi, least, run, explain, messag, havent, next., tuesday, occur, san, receiv, program, upon, start, refer, ago, prohibit, approxim, although, differ, releas, legisl, analysi, just, later, open, chairman, region, move, clear, administr, forward, post, director, decemb, produc, believ, estim, alreadi, fix, found, recent, tri, consult, facil, particip, trader, seek, see, novemb, crisi, cpuc, richard, far, entir, investig, polici, retail, indic, free, execut, respons, materi, modifi, X100, citi, X2001, steven, new, third, cover, oblig, long, portion, august, suggest, basi, idea, return, possibl, tariff, procedur, might, person, regul, readi, condit, agre, though, due, four, combin, X1999, big, someth, comput, look, averag, replac, everi, member, press, tom, record, applic, focus, way, hold, particular, short, decis, becom, jame, connect, corp, posit, thing, coupl, howev, court, manag, potenti, littl, juli, thursday, made, put, detail, begin, answer, site, well, must, total, excess, effect, enter, involv, complet, chanc, togeth, negoti, therefor, advanc, notic, ensur, brian, except, home, morn, give, act, storag, accept, coordin, unless, confer, otherwis, privat, futur, publish, share, fail, web, inform, given, arrang, that, statement, project, soon, current, second, question, first, week, william, ask, commod, say, hope, schedul, intend, jim, reach, less, case, outsid, daili, section, instead, name, place, rule, guarante, address, govern, associ, practic, subject, lot, access, trade, locat, susan, model, invest, main, build, depart, financ, note, left, identifi, internet, reflect, actual, express, bill, general, support, import, qualiti, confidenti, view, show, comment, peopl, eric, correct, work, similar, assum, fund, dasovich, late, sign, seem, seen, cash, option, tell, add, word, commerci, flow, sale, limit, attorney, previous, concern, submit, karen, approach, data, necessari, decid, various, recommend, abl, along, water, physic, claim, final, error, number, event, think, paul, alan, april, parti, exampl, els, group, anyon, dissemin, distribut, onlin, realli, south, delet, improv, sue, law, individu, oil, prepar, hear, leav, advis, head, standard, cynthia, joe, notifi, speak, thought, jan, tim, know, awar, strong, page, mean, sender, activ, organ, east, present, protect, corpor, west, kevin, enron, initi, singl, area, relat, kind, everyon, firm, electron, extend, assist, affect, octob, origin, specif, attent, septemb, deliv, chang, york, rob, hard, north, valu, termin, capit, offic, either, calcul, yet, ill, consider, side, approv, request, shall, asset, product, feel, let, carol, someon, march, probabl, sent, commit, appropri, matter, depend, research, chris, keannaenronenron, quick, contain, hand, gari, respect, steve, guy, date, deal, tomorrow, repli, latest, interest, extens, scott, financi, liquid, texa, local, intern, entiti, vinc, anyth, team, read, great, via, risk, impact, proceed, desk, ive, done, harri, llc, afternoon, mari, michael, cours, best, perform, link, delay, account, committe, opportun, privileg, process, find, sure, mail, greg, send, legal, recipi, london, transact, confirm, mention, summari, convers, prior, visit, respond, bit, yesterday, websit, format, review, paper, georg, peter, handl, kelli, item, employe, net, provis, februari, suit, frank, robert, attend, strategi, dave, email.1, street, join, dan, list, ken, met, appreci, draft, contact, status, bob, version, type, appli, sorri, mark, credit, understand, kaminskihouect, etc, rick, brief, communic, document, bank, sheet, phone, gerald, ena, copi, fyi, counsel, john, updat, jeffrey, roger, lesli, lisa, agreement, volum, letter, elizabeth, .rnorm, settlement, discuss, houston, global, info, transfer, pleas, america, europ, smith, stephani, memo, dear, regard, fax, form, counterparti, languag, mike, master, X77002, david, check, print, amend, eol, kate, attach, book, X1400, revis, thank
## 13 price, independ, demand, generat, suppli, emerg, summer, ferc, southern, util, custom, dow, high, oper, california, transmiss, natur, power, iso, jone, forc, gas, longer, capac, low, increas, expect, problem, consum, pay, system, plant, take, higher, rate, feder, said, continu, cap, commiss, sourc, solut, three, among, right, addit, order, author, line, energi, bring, push, major, load, told, can, lower, public, pipelin, peak, cost, supplier, without, electr, state, copyright, hour, interconnect, econom, two, implement, analyst, june, million, level, close, establish, want, within, purchas, other, part, announc, enough, servic, market, inc, grid, provid, competit, reduc, regulatori, news, steffesnaenronenron, creat, propos, still, situat, profit, X2000, whether, wholesal, transport, paid, fall, action, serv, rais, fuel, technolog, avail, will, today, full, unit, face, pass, compani, requir, cut, point, step, even, andor, earlier, design, past, result, pacif, sell, mani, reason, also, reserv, one, nation, time, consid, billion, percent, deregul, buy, industri, money, key, presid, day, set, remain, issu, success, plan, edison, call, accord, last, sever, own, earli, spot, includ, year, appear, bid, base, balanc, get, staff, anoth, deliveri, sinc, got, control, allow, now, resourc, jeff, small, articl, charg, per, washington, dollar, better, seller, contract, benefit, board, larg, wednesday, help, near, sold, term, turn, construct, five, much, like, around, amount, direct, period, top, structur, offici, governor, signific, meet, rather, end, friday, report, may, fact, dont, agenc, offer, use, follow, file, taken, purpos, opinion, talk, certain, grow, good, keep, altern, busi, dasovichnaenron, exist, come, make, real, drive, avoid, pge, monday, effort, effici, month, back, repres, measur, davi, determin, exchang, develop, lead, need, januari, payment, caus, immedi, least, run, explain, messag, havent, next., tuesday, occur, san, receiv, program, upon, start, refer, ago, prohibit, approxim, although, differ, releas, legisl, analysi, just, later, open, chairman, region, move, clear, administr, forward, post, director, decemb, produc, believ, estim, alreadi, fix, found, recent, tri, consult, facil, particip, trader, seek, see, novemb, crisi, cpuc, richard, far, entir, investig, polici, retail, indic, free, execut, respons, materi, modifi, X100, citi, X2001, steven, new, third, cover, oblig, long, portion, august, suggest, basi, idea, return, possibl, tariff, procedur, might, person, regul, readi, condit, agre, though, due, four, combin, X1999, big, someth, comput, look, averag, replac, everi, member, press, tom, record, applic, focus, way, hold, particular, short, decis, becom, jame, connect, corp, posit, thing, coupl, howev, court, manag, potenti, littl, juli, thursday, made, put, detail, begin, answer, site, well, must, total, excess, effect, enter, involv, complet, chanc, togeth, negoti, therefor, advanc, notic, ensur, brian, except, home, morn, give, act, storag, accept, coordin, unless, confer, otherwis, privat, futur, publish, share, fail, web, inform, given, arrang, that, statement, project, soon, current, second, question, first, week, william, ask, commod, say, hope, schedul, intend, jim, reach, less, case, outsid, daili, section, instead, name, place, rule, guarante, address, govern, associ, practic, subject, lot, access, trade, locat, susan, model, invest, main, build, depart, financ, note, left, identifi, internet, reflect, actual, express, bill, general, support, import, qualiti, confidenti, view, show, comment, peopl, eric, correct, work, similar, assum, fund, dasovich, late, sign, seem, seen, cash, option, tell, add, word, commerci, flow, sale, limit, attorney, previous, concern, submit, karen, approach, data, necessari, decid, various, recommend, abl, along, water, physic, claim, final, error, number, event, think, paul, alan, april, parti, exampl, els, group, anyon, dissemin, distribut, onlin, realli, south, delet, improv, sue, law, individu, oil, prepar, hear, leav, advis, head, standard, cynthia, joe, notifi, speak, thought, jan, tim, know, awar, strong, page, mean, sender, activ, organ, east, present, protect, corpor, west, kevin, enron, initi, singl, area, relat, kind, everyon, firm, electron, extend, assist, affect, octob, origin, specif, attent, septemb, deliv, chang, york, rob, hard, north, valu, termin, capit, offic, either, calcul, yet, ill, consider, side, approv, request, shall, asset, product, feel, let, carol, someon, march, probabl, sent, commit, appropri, matter, depend, research, chris, keannaenronenron, quick, contain, hand, gari, respect, steve, guy, date, deal, tomorrow, repli, latest, interest, extens, scott, financi, liquid, texa, local, intern, entiti, vinc, anyth, team, read, great, via, risk, impact, proceed, desk, ive, done, harri, llc, afternoon, mari, michael, cours, best, perform, link, delay, account, committe, opportun, privileg, process, find, sure, mail, greg, send, legal, recipi, london, transact, confirm, mention, summari, convers, prior, visit, respond, bit, yesterday, websit, format, review, paper, georg, peter, handl, kelli, item, employe, net, provis, februari, suit, frank, robert, attend, strategi, dave, email.1, street, join, dan, list, ken, met, appreci, draft, contact, status, bob, version, type, appli, sorri, mark, credit, understand, kaminskihouect, etc, rick, brief, communic, document, bank, sheet, phone, gerald, ena, copi, fyi, counsel, john, updat, jeffrey, roger, lesli, lisa, agreement, volum, letter, elizabeth, settlement, discuss, houston, global, info, transfer, pleas, america, europ, smith, stephani, memo, dear, regard, fax, form, counterparti, languag, mike, master, X77002, david, check, print, amend, eol, kate, attach, book, X1400, revis, thank
## max.nTuningRuns max.auc.fit opt.prob.threshold.fit max.f.score.fit
## 1 0 0.5000000 0.5 0.0000000
## 2 0 0.4898348 0.1 0.2791367
## 3 0 0.5000000 0.5 0.0000000
## 4 0 0.7284092 0.3 0.5568182
## 5 3 0.7284092 0.3 0.5568182
## 6 1 0.7433484 0.2 0.5568182
## 7 1 0.7670432 0.9 0.6933333
## 8 1 0.8493426 0.9 0.7411168
## 9 1 0.7590283 0.9 0.5204461
## 10 3 0.6952075 0.7 0.5405405
## 11 0 0.8629545 0.6 0.7282609
## 12 3 1.0000000 0.5 1.0000000
## 13 3 0.9999897 0.6 0.9948187
## max.Accuracy.fit max.Kappa.fit max.auc.OOB opt.prob.threshold.OOB
## 1 0.8377926 0.00000000 0.5000000 0.5
## 2 0.1622074 0.00000000 0.4923588 0.1
## 3 0.8377926 0.00000000 0.5000000 0.5
## 4 0.8695652 0.48128378 0.7246401 0.3
## 5 0.8779397 0.45230238 0.7246401 0.3
## 6 0.8679062 0.33325458 0.8167774 0.2
## 7 0.8361390 0.24514952 0.6553156 0.9
## 8 0.5050335 0.02266820 0.4558693 0.0
## 9 0.5452345 0.05021386 0.4581949 0.0
## 10 0.8812563 0.46413751 0.7482281 0.7
## 11 0.9163880 0.67902613 0.7936323 0.7
## 12 0.8879599 0.52679816 0.9187708 0.4
## 13 0.8846154 0.51727930 0.9169435 0.4
## max.f.score.OOB max.Accuracy.OOB max.Kappa.OOB
## 1 0.0000000 0.8365759 0.0000000
## 2 0.2809365 0.1634241 0.0000000
## 3 0.0000000 0.8365759 0.0000000
## 4 0.5301205 0.8482490 0.4396489
## 5 0.5301205 0.8482490 0.4396489
## 6 0.5301205 0.8482490 0.4396489
## 7 0.4477612 0.8560311 0.3710563
## 8 0.2809365 0.1634241 0.0000000
## 9 0.2809365 0.1634241 0.0000000
## 10 0.6052632 0.8832685 0.5376589
## 11 0.6172840 0.8793774 0.5458070
## 12 0.6185567 0.8560311 0.5317839
## 13 0.6185567 0.8560311 0.5317839
## inv.elapsedtime.everything inv.elapsedtime.final inv.aic.fit
## 1 2.100840336 500.0000000 NA
## 2 3.367003367 500.0000000 NA
## 3 1.557632399 71.4285714 NA
## 4 1.838235294 83.3333333 NA
## 5 0.979431929 83.3333333 NA
## 6 1.104972376 76.9230769 2.243950e-03
## 7 0.589970501 4.4052863 2.858763e-04
## 8 0.052091473 0.1532332 2.055730e-04
## 9 0.049566295 0.1736413 9.535377e-05
## 10 0.130718954 0.8802817 NA
## 11 0.610500611 0.8733624 NA
## 12 0.005988849 0.0169990 NA
## 13 0.006613888 0.0182249 NAprint(myplot_radar(radar_inp_df=plt_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 13. Consider specifying shapes manually. if you must have them.## Warning: Removed 6 rows containing missing values (geom_path).## Warning: Removed 104 rows containing missing values (geom_point).## Warning: Removed 9 rows containing missing values (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 13. Consider specifying shapes manually. if you must have them.
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(model_id %in% grep("random|MFO", plt_models_df$model_id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}## Warning: max.AccuracyUpper.fit already exists in glb_models_df## [1] "var:max.KappaSD.fit"# Plot metrics with CI
plt_models_df <- glb_models_df[, "model_id", FALSE]
pltCI_models_df <- glb_models_df[, "model_id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="model_id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="model_id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <- unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
#print(mltdCI_models_df)
# castCI_models_df <- dcast(mltdCI_models_df, value ~ type, fun.aggregate=sum)
# print(castCI_models_df)
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data, sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("model_id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("model_id", "model_method")], all.x=TRUE)
png(paste0(glb_out_pfx, "models_bar.png"), width=480*3, height=480*2)
print(gp <- myplot_bar(mltd_models_df, "model_id", "value", colorcol_name="model_method") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=model_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))
dev.off()## quartz_off_screen
## 2print(gp)
# used for console inspection
model_evl_terms <- c(NULL)
for (metric in glb_model_evl_criteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse=" "))
print(tmp_models_df <- orderBy(model_sel_frmla, glb_models_df)[, c("model_id", glb_model_evl_criteria)])## model_id max.Accuracy.OOB max.Kappa.OOB min.aic.fit
## 10 Conditional.X.rpart 0.8832685 0.5376589 NA
## 11 Conditional.X.cp.0.rpart 0.8793774 0.5458070 NA
## 12 Conditional.X.rf 0.8560311 0.5317839 NA
## 13 Conditional.X.no.rnorm.rf 0.8560311 0.5317839 NA
## 7 Interact.High.cor.y.glm 0.8560311 0.3710563 3498.0161
## 6 Max.cor.Y.glm 0.8482490 0.4396489 445.6427
## 4 Max.cor.Y.cv.0.cp.0.rpart 0.8482490 0.4396489 NA
## 5 Max.cor.Y.rpart 0.8482490 0.4396489 NA
## 1 MFO.myMFO_classfr 0.8365759 0.0000000 NA
## 3 Max.cor.Y.cv.0.rpart 0.8365759 0.0000000 NA
## 8 Low.cor.X.glm 0.1634241 0.0000000 4864.4526
## 9 Conditional.X.glm 0.1634241 0.0000000 10487.2626
## 2 Random.myrandom_classfr 0.1634241 0.0000000 NAprint(myplot_radar(radar_inp_df=tmp_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 13. Consider specifying shapes manually. if you must have them.## Warning: Removed 6 rows containing missing values (geom_path).## Warning: Removed 27 rows containing missing values (geom_point).## Warning: Removed 9 rows containing missing values (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 13. Consider specifying shapes manually. if you must have them.
print("Metrics used for model selection:"); print(model_sel_frmla)## [1] "Metrics used for model selection:"## ~-max.Accuracy.OOB - max.Kappa.OOB + min.aic.fitprint(sprintf("Best model id: %s", tmp_models_df[1, "model_id"]))## [1] "Best model id: Conditional.X.rpart"if (is.null(glb_sel_mdl_id))
{ glb_sel_mdl_id <- tmp_models_df[1, "model_id"] } else
print(sprintf("User specified selection: %s", glb_sel_mdl_id))
myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 598
##
## CP nsplit rel error
## 1 0.29896907 0 1.0000000
## 2 0.06185567 1 0.7010309
##
## Variable importance
## california wholesal edison said southern util
## 39 14 12 12 12 11
##
## Node number 1: 598 observations, complexity param=0.2989691
## predicted class=N expected loss=0.1622074 P(node) =1
## class counts: 501 97
## probabilities: 0.838 0.162
## left son=2 (547 obs) right son=3 (51 obs)
## Primary splits:
## california < 1.5 to the left, improve=43.15621, (0 missing)
## price < 1.5 to the left, improve=38.19520, (0 missing)
## electr < 1.5 to the left, improve=36.87857, (0 missing)
## state < 1.5 to the left, improve=33.95206, (0 missing)
## demand < 0.5 to the left, improve=31.57326, (0 missing)
## Surrogate splits:
## wholesal < 0.5 to the left, agree=0.945, adj=0.353, (0 split)
## southern < 0.5 to the left, agree=0.941, adj=0.314, (0 split)
## said < 2.5 to the left, agree=0.941, adj=0.314, (0 split)
## edison < 0.5 to the left, agree=0.941, adj=0.314, (0 split)
## util < 2.5 to the left, agree=0.940, adj=0.294, (0 split)
##
## Node number 2: 547 observations
## predicted class=N expected loss=0.1042048 P(node) =0.9147157
## class counts: 490 57
## probabilities: 0.896 0.104
##
## Node number 3: 51 observations
## predicted class=Y expected loss=0.2156863 P(node) =0.08528428
## class counts: 11 40
## probabilities: 0.216 0.784
##
## n= 598
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 598 97 N (0.8377926 0.1622074)
## 2) california< 1.5 547 57 N (0.8957952 0.1042048) *
## 3) california>=1.5 51 11 Y (0.2156863 0.7843137) *## [1] TRUEreplay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
glb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="fit.data.training.all",
chunk_step_major=max(glb_script_df$chunk_step_major)+1,
chunk_step_minor=0,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))## chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed9 fit.models 5 1 965.105
## elapsed10 fit.data.training.all 6 0 980.008Step 6: fit.data.training.all
if (!is.null(glb_fin_mdl_id) && (glb_fin_mdl_id %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_sel_mdl
} else {
print(mdl_feats_df <- myextract_mdl_feats(sel_mdl=glb_sel_mdl, entity_df=glb_trnent_df))
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the model_id
model_method <- tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)
tune_finmdl_df <- NULL
if (nrow(glb_sel_mdl$bestTune) > 0) {
for (param in names(glb_sel_mdl$bestTune)) {
#print(sprintf("param: %s", param))
tune_finmdl_df <- rbind(tune_finmdl_df,
data.frame(parameter=param,
min=glb_sel_mdl$bestTune[1, param],
max=glb_sel_mdl$bestTune[1, param],
by=1)) # by val does not matter
}
}
# Sync with parameters in mydsutils.R
ret_lst <- myfit_mdl(model_id="Final", model_method=model_method,
indep_vars_vctr=mdl_feats_df$id, model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=NULL,
n_cv_folds=glb_n_cv_folds, tune_models_df=tune_finmdl_df,
# Automate from here
# Issues if glb_sel_mdl$method == "rf" b/c trainControl is "oob"; not "cv"
model_loss_mtrx=glb_model_metric_terms,
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize)
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glb_fin_mdl_id <- glb_models_df[length(glb_models_lst), "model_id"]
}## importance id fit.feat
## california 100.00000 california TRUE
## price 88.50451 price TRUE
## electr 85.45367 electr TRUE
## state 78.67248 state TRUE
## demand 73.16040 demand TRUE
## independ 0.00000 independ TRUE
## generat 0.00000 generat TRUE
## suppli 0.00000 suppli TRUE
## emerg 0.00000 emerg TRUE
## summer 0.00000 summer TRUE
## ferc 0.00000 ferc TRUE
## southern 0.00000 southern TRUE
## util 0.00000 util TRUE
## custom 0.00000 custom TRUE
## dow 0.00000 dow TRUE
## high 0.00000 high TRUE
## oper 0.00000 oper TRUE
## transmiss 0.00000 transmiss TRUE
## natur 0.00000 natur TRUE
## power 0.00000 power TRUE
## iso 0.00000 iso TRUE
## jone 0.00000 jone TRUE
## forc 0.00000 forc TRUE
## gas 0.00000 gas TRUE
## longer 0.00000 longer TRUE
## capac 0.00000 capac TRUE
## low 0.00000 low TRUE
## increas 0.00000 increas TRUE
## expect 0.00000 expect TRUE
## problem 0.00000 problem TRUE
## consum 0.00000 consum TRUE
## pay 0.00000 pay TRUE
## system 0.00000 system TRUE
## plant 0.00000 plant TRUE
## take 0.00000 take TRUE
## higher 0.00000 higher TRUE
## rate 0.00000 rate TRUE
## feder 0.00000 feder TRUE
## said 0.00000 said TRUE
## continu 0.00000 continu TRUE
## cap 0.00000 cap TRUE
## commiss 0.00000 commiss TRUE
## sourc 0.00000 sourc TRUE
## solut 0.00000 solut TRUE
## three 0.00000 three TRUE
## among 0.00000 among TRUE
## right 0.00000 right TRUE
## addit 0.00000 addit TRUE
## order 0.00000 order TRUE
## author 0.00000 author TRUE
## line 0.00000 line TRUE
## energi 0.00000 energi TRUE
## bring 0.00000 bring TRUE
## push 0.00000 push TRUE
## major 0.00000 major TRUE
## load 0.00000 load TRUE
## told 0.00000 told TRUE
## can 0.00000 can TRUE
## lower 0.00000 lower TRUE
## public 0.00000 public TRUE
## pipelin 0.00000 pipelin TRUE
## peak 0.00000 peak TRUE
## cost 0.00000 cost TRUE
## supplier 0.00000 supplier TRUE
## without 0.00000 without TRUE
## copyright 0.00000 copyright TRUE
## hour 0.00000 hour TRUE
## interconnect 0.00000 interconnect TRUE
## econom 0.00000 econom TRUE
## two 0.00000 two TRUE
## implement 0.00000 implement TRUE
## analyst 0.00000 analyst TRUE
## june 0.00000 june TRUE
## million 0.00000 million TRUE
## level 0.00000 level TRUE
## close 0.00000 close TRUE
## establish 0.00000 establish TRUE
## want 0.00000 want TRUE
## within 0.00000 within TRUE
## purchas 0.00000 purchas TRUE
## other 0.00000 other TRUE
## part 0.00000 part TRUE
## announc 0.00000 announc TRUE
## enough 0.00000 enough TRUE
## servic 0.00000 servic TRUE
## market 0.00000 market TRUE
## inc 0.00000 inc TRUE
## grid 0.00000 grid TRUE
## provid 0.00000 provid TRUE
## competit 0.00000 competit TRUE
## reduc 0.00000 reduc TRUE
## regulatori 0.00000 regulatori TRUE
## news 0.00000 news TRUE
## steffesnaenronenron 0.00000 steffesnaenronenron TRUE
## creat 0.00000 creat TRUE
## propos 0.00000 propos TRUE
## still 0.00000 still TRUE
## situat 0.00000 situat TRUE
## profit 0.00000 profit TRUE
## X2000 0.00000 X2000 TRUE
## whether 0.00000 whether TRUE
## wholesal 0.00000 wholesal TRUE
## transport 0.00000 transport TRUE
## paid 0.00000 paid TRUE
## fall 0.00000 fall TRUE
## action 0.00000 action TRUE
## serv 0.00000 serv TRUE
## rais 0.00000 rais TRUE
## fuel 0.00000 fuel TRUE
## technolog 0.00000 technolog TRUE
## avail 0.00000 avail TRUE
## will 0.00000 will TRUE
## today 0.00000 today TRUE
## full 0.00000 full TRUE
## unit 0.00000 unit TRUE
## face 0.00000 face TRUE
## pass 0.00000 pass TRUE
## compani 0.00000 compani TRUE
## requir 0.00000 requir TRUE
## cut 0.00000 cut TRUE
## point 0.00000 point TRUE
## step 0.00000 step TRUE
## even 0.00000 even TRUE
## andor 0.00000 andor TRUE
## earlier 0.00000 earlier TRUE
## design 0.00000 design TRUE
## past 0.00000 past TRUE
## result 0.00000 result TRUE
## pacif 0.00000 pacif TRUE
## sell 0.00000 sell TRUE
## mani 0.00000 mani TRUE
## reason 0.00000 reason TRUE
## also 0.00000 also TRUE
## reserv 0.00000 reserv TRUE
## one 0.00000 one TRUE
## nation 0.00000 nation TRUE
## time 0.00000 time TRUE
## consid 0.00000 consid TRUE
## billion 0.00000 billion TRUE
## percent 0.00000 percent TRUE
## deregul 0.00000 deregul TRUE
## buy 0.00000 buy TRUE
## industri 0.00000 industri TRUE
## money 0.00000 money TRUE
## key 0.00000 key TRUE
## presid 0.00000 presid TRUE
## day 0.00000 day TRUE
## set 0.00000 set TRUE
## remain 0.00000 remain TRUE
## issu 0.00000 issu TRUE
## success 0.00000 success TRUE
## plan 0.00000 plan TRUE
## edison 0.00000 edison TRUE
## call 0.00000 call TRUE
## accord 0.00000 accord TRUE
## last 0.00000 last TRUE
## sever 0.00000 sever TRUE
## own 0.00000 own TRUE
## earli 0.00000 earli TRUE
## spot 0.00000 spot TRUE
## includ 0.00000 includ TRUE
## year 0.00000 year TRUE
## appear 0.00000 appear TRUE
## bid 0.00000 bid TRUE
## base 0.00000 base TRUE
## balanc 0.00000 balanc TRUE
## get 0.00000 get TRUE
## staff 0.00000 staff TRUE
## anoth 0.00000 anoth TRUE
## deliveri 0.00000 deliveri TRUE
## sinc 0.00000 sinc TRUE
## got 0.00000 got TRUE
## control 0.00000 control TRUE
## allow 0.00000 allow TRUE
## now 0.00000 now TRUE
## resourc 0.00000 resourc TRUE
## jeff 0.00000 jeff TRUE
## small 0.00000 small TRUE
## articl 0.00000 articl TRUE
## charg 0.00000 charg TRUE
## per 0.00000 per TRUE
## washington 0.00000 washington TRUE
## dollar 0.00000 dollar TRUE
## better 0.00000 better TRUE
## seller 0.00000 seller TRUE
## contract 0.00000 contract TRUE
## benefit 0.00000 benefit TRUE
## board 0.00000 board TRUE
## larg 0.00000 larg TRUE
## wednesday 0.00000 wednesday TRUE
## help 0.00000 help TRUE
## near 0.00000 near TRUE
## sold 0.00000 sold TRUE
## term 0.00000 term TRUE
## turn 0.00000 turn TRUE
## construct 0.00000 construct TRUE
## five 0.00000 five TRUE
## much 0.00000 much TRUE
## like 0.00000 like TRUE
## around 0.00000 around TRUE
## amount 0.00000 amount TRUE
## direct 0.00000 direct TRUE
## period 0.00000 period TRUE
## top 0.00000 top TRUE
## structur 0.00000 structur TRUE
## offici 0.00000 offici TRUE
## governor 0.00000 governor TRUE
## signific 0.00000 signific TRUE
## meet 0.00000 meet TRUE
## rather 0.00000 rather TRUE
## end 0.00000 end TRUE
## friday 0.00000 friday TRUE
## report 0.00000 report TRUE
## may 0.00000 may TRUE
## fact 0.00000 fact TRUE
## dont 0.00000 dont TRUE
## agenc 0.00000 agenc TRUE
## offer 0.00000 offer TRUE
## use 0.00000 use TRUE
## follow 0.00000 follow TRUE
## file 0.00000 file TRUE
## taken 0.00000 taken TRUE
## purpos 0.00000 purpos TRUE
## opinion 0.00000 opinion TRUE
## talk 0.00000 talk TRUE
## certain 0.00000 certain TRUE
## grow 0.00000 grow TRUE
## good 0.00000 good TRUE
## keep 0.00000 keep TRUE
## altern 0.00000 altern TRUE
## busi 0.00000 busi TRUE
## dasovichnaenron 0.00000 dasovichnaenron TRUE
## exist 0.00000 exist TRUE
## come 0.00000 come TRUE
## make 0.00000 make TRUE
## real 0.00000 real TRUE
## drive 0.00000 drive TRUE
## avoid 0.00000 avoid TRUE
## pge 0.00000 pge TRUE
## monday 0.00000 monday TRUE
## effort 0.00000 effort TRUE
## effici 0.00000 effici TRUE
## month 0.00000 month TRUE
## back 0.00000 back TRUE
## repres 0.00000 repres TRUE
## measur 0.00000 measur TRUE
## davi 0.00000 davi TRUE
## determin 0.00000 determin TRUE
## exchang 0.00000 exchang TRUE
## develop 0.00000 develop TRUE
## lead 0.00000 lead TRUE
## need 0.00000 need TRUE
## januari 0.00000 januari TRUE
## payment 0.00000 payment TRUE
## caus 0.00000 caus TRUE
## immedi 0.00000 immedi TRUE
## least 0.00000 least TRUE
## run 0.00000 run TRUE
## explain 0.00000 explain TRUE
## messag 0.00000 messag TRUE
## havent 0.00000 havent TRUE
## next. 0.00000 next. TRUE
## tuesday 0.00000 tuesday TRUE
## occur 0.00000 occur TRUE
## san 0.00000 san TRUE
## receiv 0.00000 receiv TRUE
## program 0.00000 program TRUE
## upon 0.00000 upon TRUE
## start 0.00000 start TRUE
## refer 0.00000 refer TRUE
## ago 0.00000 ago TRUE
## prohibit 0.00000 prohibit TRUE
## approxim 0.00000 approxim TRUE
## although 0.00000 although TRUE
## differ 0.00000 differ TRUE
## releas 0.00000 releas TRUE
## legisl 0.00000 legisl TRUE
## analysi 0.00000 analysi TRUE
## just 0.00000 just TRUE
## later 0.00000 later TRUE
## open 0.00000 open TRUE
## chairman 0.00000 chairman TRUE
## region 0.00000 region TRUE
## move 0.00000 move TRUE
## clear 0.00000 clear TRUE
## administr 0.00000 administr TRUE
## forward 0.00000 forward TRUE
## post 0.00000 post TRUE
## director 0.00000 director TRUE
## decemb 0.00000 decemb TRUE
## produc 0.00000 produc TRUE
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## see 0.00000 see TRUE
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## far 0.00000 far TRUE
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## retail 0.00000 retail TRUE
## indic 0.00000 indic TRUE
## free 0.00000 free TRUE
## execut 0.00000 execut TRUE
## respons 0.00000 respons TRUE
## materi 0.00000 materi TRUE
## modifi 0.00000 modifi TRUE
## X100 0.00000 X100 TRUE
## citi 0.00000 citi TRUE
## X2001 0.00000 X2001 TRUE
## steven 0.00000 steven TRUE
## new 0.00000 new TRUE
## third 0.00000 third TRUE
## cover 0.00000 cover TRUE
## oblig 0.00000 oblig TRUE
## long 0.00000 long TRUE
## portion 0.00000 portion TRUE
## august 0.00000 august TRUE
## suggest 0.00000 suggest TRUE
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## idea 0.00000 idea TRUE
## return 0.00000 return TRUE
## possibl 0.00000 possibl TRUE
## tariff 0.00000 tariff TRUE
## procedur 0.00000 procedur TRUE
## might 0.00000 might TRUE
## person 0.00000 person TRUE
## regul 0.00000 regul TRUE
## readi 0.00000 readi TRUE
## condit 0.00000 condit TRUE
## agre 0.00000 agre TRUE
## though 0.00000 though TRUE
## due 0.00000 due TRUE
## four 0.00000 four TRUE
## combin 0.00000 combin TRUE
## X1999 0.00000 X1999 TRUE
## big 0.00000 big TRUE
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## look 0.00000 look TRUE
## averag 0.00000 averag TRUE
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## everi 0.00000 everi TRUE
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## tom 0.00000 tom TRUE
## record 0.00000 record TRUE
## applic 0.00000 applic TRUE
## focus 0.00000 focus TRUE
## way 0.00000 way TRUE
## hold 0.00000 hold TRUE
## particular 0.00000 particular TRUE
## short 0.00000 short TRUE
## decis 0.00000 decis TRUE
## becom 0.00000 becom TRUE
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## connect 0.00000 connect TRUE
## corp 0.00000 corp TRUE
## posit 0.00000 posit TRUE
## thing 0.00000 thing TRUE
## coupl 0.00000 coupl TRUE
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## court 0.00000 court TRUE
## manag 0.00000 manag TRUE
## potenti 0.00000 potenti TRUE
## littl 0.00000 littl TRUE
## juli 0.00000 juli TRUE
## thursday 0.00000 thursday TRUE
## made 0.00000 made TRUE
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## detail 0.00000 detail TRUE
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## well 0.00000 well TRUE
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## except 0.00000 except TRUE
## home 0.00000 home TRUE
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## give 0.00000 give TRUE
## act 0.00000 act TRUE
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## accept 0.00000 accept TRUE
## coordin 0.00000 coordin TRUE
## unless 0.00000 unless TRUE
## confer 0.00000 confer TRUE
## otherwis 0.00000 otherwis TRUE
## privat 0.00000 privat TRUE
## futur 0.00000 futur TRUE
## publish 0.00000 publish TRUE
## share 0.00000 share TRUE
## fail 0.00000 fail TRUE
## web 0.00000 web TRUE
## inform 0.00000 inform TRUE
## given 0.00000 given TRUE
## arrang 0.00000 arrang TRUE
## that 0.00000 that TRUE
## statement 0.00000 statement TRUE
## project 0.00000 project TRUE
## soon 0.00000 soon TRUE
## current 0.00000 current TRUE
## second 0.00000 second TRUE
## question 0.00000 question TRUE
## first 0.00000 first TRUE
## week 0.00000 week TRUE
## william 0.00000 william TRUE
## ask 0.00000 ask TRUE
## commod 0.00000 commod TRUE
## say 0.00000 say TRUE
## hope 0.00000 hope TRUE
## schedul 0.00000 schedul TRUE
## intend 0.00000 intend TRUE
## jim 0.00000 jim TRUE
## reach 0.00000 reach TRUE
## less 0.00000 less TRUE
## case 0.00000 case TRUE
## outsid 0.00000 outsid TRUE
## daili 0.00000 daili TRUE
## section 0.00000 section TRUE
## instead 0.00000 instead TRUE
## name 0.00000 name TRUE
## place 0.00000 place TRUE
## rule 0.00000 rule TRUE
## guarante 0.00000 guarante TRUE
## address 0.00000 address TRUE
## govern 0.00000 govern TRUE
## associ 0.00000 associ TRUE
## practic 0.00000 practic TRUE
## subject 0.00000 subject TRUE
## lot 0.00000 lot TRUE
## access 0.00000 access TRUE
## trade 0.00000 trade TRUE
## locat 0.00000 locat TRUE
## susan 0.00000 susan TRUE
## model 0.00000 model TRUE
## invest 0.00000 invest TRUE
## main 0.00000 main TRUE
## build 0.00000 build TRUE
## depart 0.00000 depart TRUE
## financ 0.00000 financ TRUE
## note 0.00000 note TRUE
## left 0.00000 left TRUE
## identifi 0.00000 identifi TRUE
## internet 0.00000 internet TRUE
## reflect 0.00000 reflect TRUE
## actual 0.00000 actual TRUE
## express 0.00000 express TRUE
## bill 0.00000 bill TRUE
## general 0.00000 general TRUE
## support 0.00000 support TRUE
## import 0.00000 import TRUE
## qualiti 0.00000 qualiti TRUE
## confidenti 0.00000 confidenti TRUE
## view 0.00000 view TRUE
## show 0.00000 show TRUE
## comment 0.00000 comment TRUE
## peopl 0.00000 peopl TRUE
## eric 0.00000 eric TRUE
## correct 0.00000 correct TRUE
## work 0.00000 work TRUE
## similar 0.00000 similar TRUE
## assum 0.00000 assum TRUE
## fund 0.00000 fund TRUE
## dasovich 0.00000 dasovich TRUE
## late 0.00000 late TRUE
## sign 0.00000 sign TRUE
## seem 0.00000 seem TRUE
## seen 0.00000 seen TRUE
## cash 0.00000 cash TRUE
## option 0.00000 option TRUE
## tell 0.00000 tell TRUE
## add 0.00000 add TRUE
## word 0.00000 word TRUE
## commerci 0.00000 commerci TRUE
## flow 0.00000 flow TRUE
## sale 0.00000 sale TRUE
## limit 0.00000 limit TRUE
## attorney 0.00000 attorney TRUE
## previous 0.00000 previous TRUE
## concern 0.00000 concern TRUE
## submit 0.00000 submit TRUE
## karen 0.00000 karen TRUE
## approach 0.00000 approach TRUE
## data 0.00000 data TRUE
## necessari 0.00000 necessari TRUE
## decid 0.00000 decid TRUE
## various 0.00000 various TRUE
## recommend 0.00000 recommend TRUE
## abl 0.00000 abl TRUE
## along 0.00000 along TRUE
## water 0.00000 water TRUE
## physic 0.00000 physic TRUE
## claim 0.00000 claim TRUE
## final 0.00000 final TRUE
## error 0.00000 error TRUE
## number 0.00000 number TRUE
## event 0.00000 event TRUE
## think 0.00000 think TRUE
## paul 0.00000 paul TRUE
## alan 0.00000 alan TRUE
## april 0.00000 april TRUE
## parti 0.00000 parti TRUE
## exampl 0.00000 exampl TRUE
## els 0.00000 els TRUE
## group 0.00000 group TRUE
## anyon 0.00000 anyon TRUE
## dissemin 0.00000 dissemin TRUE
## distribut 0.00000 distribut TRUE
## onlin 0.00000 onlin TRUE
## realli 0.00000 realli TRUE
## south 0.00000 south TRUE
## delet 0.00000 delet TRUE
## improv 0.00000 improv TRUE
## sue 0.00000 sue TRUE
## law 0.00000 law TRUE
## individu 0.00000 individu TRUE
## oil 0.00000 oil TRUE
## prepar 0.00000 prepar TRUE
## hear 0.00000 hear TRUE
## leav 0.00000 leav TRUE
## advis 0.00000 advis TRUE
## head 0.00000 head TRUE
## standard 0.00000 standard TRUE
## cynthia 0.00000 cynthia TRUE
## joe 0.00000 joe TRUE
## notifi 0.00000 notifi TRUE
## speak 0.00000 speak TRUE
## thought 0.00000 thought TRUE
## jan 0.00000 jan TRUE
## tim 0.00000 tim TRUE
## know 0.00000 know TRUE
## awar 0.00000 awar TRUE
## strong 0.00000 strong TRUE
## page 0.00000 page TRUE
## mean 0.00000 mean TRUE
## sender 0.00000 sender TRUE
## activ 0.00000 activ TRUE
## organ 0.00000 organ TRUE
## east 0.00000 east TRUE
## present 0.00000 present TRUE
## protect 0.00000 protect TRUE
## corpor 0.00000 corpor TRUE
## west 0.00000 west TRUE
## kevin 0.00000 kevin TRUE
## enron 0.00000 enron TRUE
## initi 0.00000 initi TRUE
## singl 0.00000 singl TRUE
## area 0.00000 area TRUE
## relat 0.00000 relat TRUE
## kind 0.00000 kind TRUE
## everyon 0.00000 everyon TRUE
## firm 0.00000 firm TRUE
## electron 0.00000 electron TRUE
## extend 0.00000 extend TRUE
## assist 0.00000 assist TRUE
## affect 0.00000 affect TRUE
## octob 0.00000 octob TRUE
## origin 0.00000 origin TRUE
## specif 0.00000 specif TRUE
## attent 0.00000 attent TRUE
## septemb 0.00000 septemb TRUE
## deliv 0.00000 deliv TRUE
## chang 0.00000 chang TRUE
## york 0.00000 york TRUE
## rob 0.00000 rob TRUE
## hard 0.00000 hard TRUE
## north 0.00000 north TRUE
## valu 0.00000 valu TRUE
## termin 0.00000 termin TRUE
## capit 0.00000 capit TRUE
## offic 0.00000 offic TRUE
## either 0.00000 either TRUE
## calcul 0.00000 calcul TRUE
## yet 0.00000 yet TRUE
## ill 0.00000 ill TRUE
## consider 0.00000 consider TRUE
## side 0.00000 side TRUE
## approv 0.00000 approv TRUE
## request 0.00000 request TRUE
## shall 0.00000 shall TRUE
## asset 0.00000 asset TRUE
## product 0.00000 product TRUE
## feel 0.00000 feel TRUE
## let 0.00000 let TRUE
## carol 0.00000 carol TRUE
## someon 0.00000 someon TRUE
## march 0.00000 march TRUE
## probabl 0.00000 probabl TRUE
## sent 0.00000 sent TRUE
## commit 0.00000 commit TRUE
## appropri 0.00000 appropri TRUE
## matter 0.00000 matter TRUE
## depend 0.00000 depend TRUE
## research 0.00000 research TRUE
## chris 0.00000 chris TRUE
## keannaenronenron 0.00000 keannaenronenron TRUE
## quick 0.00000 quick TRUE
## contain 0.00000 contain TRUE
## hand 0.00000 hand TRUE
## gari 0.00000 gari TRUE
## respect 0.00000 respect TRUE
## steve 0.00000 steve TRUE
## guy 0.00000 guy TRUE
## date 0.00000 date TRUE
## deal 0.00000 deal TRUE
## tomorrow 0.00000 tomorrow TRUE
## repli 0.00000 repli TRUE
## latest 0.00000 latest TRUE
## interest 0.00000 interest TRUE
## extens 0.00000 extens TRUE
## scott 0.00000 scott TRUE
## financi 0.00000 financi TRUE
## liquid 0.00000 liquid TRUE
## texa 0.00000 texa TRUE
## local 0.00000 local TRUE
## intern 0.00000 intern TRUE
## entiti 0.00000 entiti TRUE
## vinc 0.00000 vinc TRUE
## anyth 0.00000 anyth TRUE
## team 0.00000 team TRUE
## read 0.00000 read TRUE
## great 0.00000 great TRUE
## via 0.00000 via TRUE
## risk 0.00000 risk TRUE
## impact 0.00000 impact TRUE
## proceed 0.00000 proceed TRUE
## desk 0.00000 desk TRUE
## ive 0.00000 ive TRUE
## done 0.00000 done TRUE
## harri 0.00000 harri TRUE
## llc 0.00000 llc TRUE
## afternoon 0.00000 afternoon TRUE
## mari 0.00000 mari TRUE
## michael 0.00000 michael TRUE
## cours 0.00000 cours TRUE
## best 0.00000 best TRUE
## perform 0.00000 perform TRUE
## link 0.00000 link TRUE
## delay 0.00000 delay TRUE
## account 0.00000 account TRUE
## committe 0.00000 committe TRUE
## opportun 0.00000 opportun TRUE
## privileg 0.00000 privileg TRUE
## process 0.00000 process TRUE
## find 0.00000 find TRUE
## sure 0.00000 sure TRUE
## mail 0.00000 mail TRUE
## greg 0.00000 greg TRUE
## send 0.00000 send TRUE
## legal 0.00000 legal TRUE
## recipi 0.00000 recipi TRUE
## london 0.00000 london TRUE
## transact 0.00000 transact TRUE
## confirm 0.00000 confirm TRUE
## mention 0.00000 mention TRUE
## summari 0.00000 summari TRUE
## convers 0.00000 convers TRUE
## prior 0.00000 prior TRUE
## visit 0.00000 visit TRUE
## respond 0.00000 respond TRUE
## bit 0.00000 bit TRUE
## yesterday 0.00000 yesterday TRUE
## websit 0.00000 websit TRUE
## format 0.00000 format TRUE
## review 0.00000 review TRUE
## paper 0.00000 paper TRUE
## georg 0.00000 georg TRUE
## peter 0.00000 peter TRUE
## handl 0.00000 handl TRUE
## kelli 0.00000 kelli TRUE
## item 0.00000 item TRUE
## employe 0.00000 employe TRUE
## net 0.00000 net TRUE
## provis 0.00000 provis TRUE
## februari 0.00000 februari TRUE
## suit 0.00000 suit TRUE
## frank 0.00000 frank TRUE
## robert 0.00000 robert TRUE
## attend 0.00000 attend TRUE
## strategi 0.00000 strategi TRUE
## dave 0.00000 dave TRUE
## email.1 0.00000 email.1 TRUE
## street 0.00000 street TRUE
## join 0.00000 join TRUE
## dan 0.00000 dan TRUE
## list 0.00000 list TRUE
## ken 0.00000 ken TRUE
## met 0.00000 met TRUE
## appreci 0.00000 appreci TRUE
## draft 0.00000 draft TRUE
## contact 0.00000 contact TRUE
## status 0.00000 status TRUE
## bob 0.00000 bob TRUE
## version 0.00000 version TRUE
## type 0.00000 type TRUE
## appli 0.00000 appli TRUE
## sorri 0.00000 sorri TRUE
## mark 0.00000 mark TRUE
## credit 0.00000 credit TRUE
## understand 0.00000 understand TRUE
## kaminskihouect 0.00000 kaminskihouect TRUE
## etc 0.00000 etc TRUE
## rick 0.00000 rick TRUE
## brief 0.00000 brief TRUE
## communic 0.00000 communic TRUE
## document 0.00000 document TRUE
## bank 0.00000 bank TRUE
## sheet 0.00000 sheet TRUE
## phone 0.00000 phone TRUE
## gerald 0.00000 gerald TRUE
## ena 0.00000 ena TRUE
## copi 0.00000 copi TRUE
## fyi 0.00000 fyi TRUE
## counsel 0.00000 counsel TRUE
## john 0.00000 john TRUE
## updat 0.00000 updat TRUE
## jeffrey 0.00000 jeffrey TRUE
## roger 0.00000 roger TRUE
## lesli 0.00000 lesli TRUE
## lisa 0.00000 lisa TRUE
## agreement 0.00000 agreement TRUE
## volum 0.00000 volum TRUE
## letter 0.00000 letter TRUE
## elizabeth 0.00000 elizabeth TRUE
## settlement 0.00000 settlement TRUE
## discuss 0.00000 discuss TRUE
## houston 0.00000 houston TRUE
## global 0.00000 global TRUE
## info 0.00000 info TRUE
## transfer 0.00000 transfer TRUE
## pleas 0.00000 pleas TRUE
## america 0.00000 america TRUE
## europ 0.00000 europ TRUE
## smith 0.00000 smith TRUE
## stephani 0.00000 stephani TRUE
## memo 0.00000 memo TRUE
## dear 0.00000 dear TRUE
## regard 0.00000 regard TRUE
## fax 0.00000 fax TRUE
## form 0.00000 form TRUE
## counterparti 0.00000 counterparti TRUE
## languag 0.00000 languag TRUE
## mike 0.00000 mike TRUE
## master 0.00000 master TRUE
## X77002 0.00000 X77002 TRUE
## david 0.00000 david TRUE
## check 0.00000 check TRUE
## print 0.00000 print TRUE
## amend 0.00000 amend TRUE
## eol 0.00000 eol TRUE
## kate 0.00000 kate TRUE
## attach 0.00000 attach TRUE
## book 0.00000 book TRUE
## X1400 0.00000 X1400 TRUE
## revis 0.00000 revis TRUE
## thank 0.00000 thank TRUE
## [1] "fitting model: Final.rpart"
## [1] " indep_vars: california, price, electr, state, demand, independ, generat, suppli, emerg, summer, ferc, southern, util, custom, dow, high, oper, transmiss, natur, power, iso, jone, forc, gas, longer, capac, low, increas, expect, problem, consum, pay, system, plant, take, higher, rate, feder, said, continu, cap, commiss, sourc, solut, three, among, right, addit, order, author, line, energi, bring, push, major, load, told, can, lower, public, pipelin, peak, cost, supplier, without, copyright, hour, interconnect, econom, two, implement, analyst, june, million, level, close, establish, want, within, purchas, other, part, announc, enough, servic, market, inc, grid, provid, competit, reduc, regulatori, news, steffesnaenronenron, creat, propos, still, situat, profit, X2000, whether, wholesal, transport, paid, fall, action, serv, rais, fuel, technolog, avail, will, today, full, unit, face, pass, compani, requir, cut, point, step, even, andor, earlier, design, past, result, pacif, sell, mani, reason, also, reserv, one, nation, time, consid, billion, percent, deregul, buy, industri, money, key, presid, day, set, remain, issu, success, plan, edison, call, accord, last, sever, own, earli, spot, includ, year, appear, bid, base, balanc, get, staff, anoth, deliveri, sinc, got, control, allow, now, resourc, jeff, small, articl, charg, per, washington, dollar, better, seller, contract, benefit, board, larg, wednesday, help, near, sold, term, turn, construct, five, much, like, around, amount, direct, period, top, structur, offici, governor, signific, meet, rather, end, friday, report, may, fact, dont, agenc, offer, use, follow, file, taken, purpos, opinion, talk, certain, grow, good, keep, altern, busi, dasovichnaenron, exist, come, make, real, drive, avoid, pge, monday, effort, effici, month, back, repres, measur, davi, determin, exchang, develop, lead, need, januari, payment, caus, immedi, least, run, explain, messag, havent, next., tuesday, occur, san, receiv, program, upon, start, refer, ago, prohibit, approxim, although, differ, releas, legisl, analysi, just, later, open, chairman, region, move, clear, administr, forward, post, director, decemb, produc, believ, estim, alreadi, fix, found, recent, tri, consult, facil, particip, trader, seek, see, novemb, crisi, cpuc, richard, far, entir, investig, polici, retail, indic, free, execut, respons, materi, modifi, X100, citi, X2001, steven, new, third, cover, oblig, long, portion, august, suggest, basi, idea, return, possibl, tariff, procedur, might, person, regul, readi, condit, agre, though, due, four, combin, X1999, big, someth, comput, look, averag, replac, everi, member, press, tom, record, applic, focus, way, hold, particular, short, decis, becom, jame, connect, corp, posit, thing, coupl, howev, court, manag, potenti, littl, juli, thursday, made, put, detail, begin, answer, site, well, must, total, excess, effect, enter, involv, complet, chanc, togeth, negoti, therefor, advanc, notic, ensur, brian, except, home, morn, give, act, storag, accept, coordin, unless, confer, otherwis, privat, futur, publish, share, fail, web, inform, given, arrang, that, statement, project, soon, current, second, question, first, week, william, ask, commod, say, hope, schedul, intend, jim, reach, less, case, outsid, daili, section, instead, name, place, rule, guarante, address, govern, associ, practic, subject, lot, access, trade, locat, susan, model, invest, main, build, depart, financ, note, left, identifi, internet, reflect, actual, express, bill, general, support, import, qualiti, confidenti, view, show, comment, peopl, eric, correct, work, similar, assum, fund, dasovich, late, sign, seem, seen, cash, option, tell, add, word, commerci, flow, sale, limit, attorney, previous, concern, submit, karen, approach, data, necessari, decid, various, recommend, abl, along, water, physic, claim, final, error, number, event, think, paul, alan, april, parti, exampl, els, group, anyon, dissemin, distribut, onlin, realli, south, delet, improv, sue, law, individu, oil, prepar, hear, leav, advis, head, standard, cynthia, joe, notifi, speak, thought, jan, tim, know, awar, strong, page, mean, sender, activ, organ, east, present, protect, corpor, west, kevin, enron, initi, singl, area, relat, kind, everyon, firm, electron, extend, assist, affect, octob, origin, specif, attent, septemb, deliv, chang, york, rob, hard, north, valu, termin, capit, offic, either, calcul, yet, ill, consider, side, approv, request, shall, asset, product, feel, let, carol, someon, march, probabl, sent, commit, appropri, matter, depend, research, chris, keannaenronenron, quick, contain, hand, gari, respect, steve, guy, date, deal, tomorrow, repli, latest, interest, extens, scott, financi, liquid, texa, local, intern, entiti, vinc, anyth, team, read, great, via, risk, impact, proceed, desk, ive, done, harri, llc, afternoon, mari, michael, cours, best, perform, link, delay, account, committe, opportun, privileg, process, find, sure, mail, greg, send, legal, recipi, london, transact, confirm, mention, summari, convers, prior, visit, respond, bit, yesterday, websit, format, review, paper, georg, peter, handl, kelli, item, employe, net, provis, februari, suit, frank, robert, attend, strategi, dave, email.1, street, join, dan, list, ken, met, appreci, draft, contact, status, bob, version, type, appli, sorri, mark, credit, understand, kaminskihouect, etc, rick, brief, communic, document, bank, sheet, phone, gerald, ena, copi, fyi, counsel, john, updat, jeffrey, roger, lesli, lisa, agreement, volum, letter, elizabeth, settlement, discuss, houston, global, info, transfer, pleas, america, europ, smith, stephani, memo, dear, regard, fax, form, counterparti, languag, mike, master, X77002, david, check, print, amend, eol, kate, attach, book, X1400, revis, thank"
## + Fold1: cp=0.06186
## - Fold1: cp=0.06186
## + Fold2: cp=0.06186
## - Fold2: cp=0.06186
## + Fold3: cp=0.06186
## - Fold3: cp=0.06186
## Aggregating results
## Fitting final model on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 598
##
## CP nsplit rel error
## 1 0.29896907 0 1.0000000
## 2 0.06185567 1 0.7010309
##
## Variable importance
## california wholesal edison said southern util
## 39 14 12 12 12 11
##
## Node number 1: 598 observations, complexity param=0.2989691
## predicted class=N expected loss=0.1622074 P(node) =1
## class counts: 501 97
## probabilities: 0.838 0.162
## left son=2 (547 obs) right son=3 (51 obs)
## Primary splits:
## california < 1.5 to the left, improve=43.15621, (0 missing)
## price < 1.5 to the left, improve=38.19520, (0 missing)
## electr < 1.5 to the left, improve=36.87857, (0 missing)
## state < 1.5 to the left, improve=33.95206, (0 missing)
## demand < 0.5 to the left, improve=31.57326, (0 missing)
## Surrogate splits:
## wholesal < 0.5 to the left, agree=0.945, adj=0.353, (0 split)
## southern < 0.5 to the left, agree=0.941, adj=0.314, (0 split)
## said < 2.5 to the left, agree=0.941, adj=0.314, (0 split)
## edison < 0.5 to the left, agree=0.941, adj=0.314, (0 split)
## util < 2.5 to the left, agree=0.940, adj=0.294, (0 split)
##
## Node number 2: 547 observations
## predicted class=N expected loss=0.1042048 P(node) =0.9147157
## class counts: 490 57
## probabilities: 0.896 0.104
##
## Node number 3: 51 observations
## predicted class=Y expected loss=0.2156863 P(node) =0.08528428
## class counts: 11 40
## probabilities: 0.216 0.784
##
## n= 598
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 598 97 N (0.8377926 0.1622074)
## 2) california< 1.5 547 57 N (0.8957952 0.1042048) *
## 3) california>=1.5 51 11 Y (0.2156863 0.7843137) *
## Reference
## Prediction N Y
## N 0 0
## Y 501 97
## responsive.fctr responsive.fctr.predict.Final.rpart.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Final.rpart.Y
## 1 501
## 2 97
## Reference
## Prediction N Y
## N 0 0
## Y 501 97
## responsive.fctr responsive.fctr.predict.Final.rpart.N
## 1 N 0
## 2 Y 0
## responsive.fctr.predict.Final.rpart.Y
## 1 501
## 2 97
## Reference
## Prediction N Y
## N 490 57
## Y 11 40
## responsive.fctr responsive.fctr.predict.Final.rpart.N
## 1 N 490
## 2 Y 57
## responsive.fctr.predict.Final.rpart.Y
## 1 11
## 2 40
## Reference
## Prediction N Y
## N 490 57
## Y 11 40
## responsive.fctr responsive.fctr.predict.Final.rpart.N
## 1 N 490
## 2 Y 57
## responsive.fctr.predict.Final.rpart.Y
## 1 11
## 2 40
## Reference
## Prediction N Y
## N 490 57
## Y 11 40
## responsive.fctr responsive.fctr.predict.Final.rpart.N
## 1 N 490
## 2 Y 57
## responsive.fctr.predict.Final.rpart.Y
## 1 11
## 2 40
## Reference
## Prediction N Y
## N 490 57
## Y 11 40
## responsive.fctr responsive.fctr.predict.Final.rpart.N
## 1 N 490
## 2 Y 57
## responsive.fctr.predict.Final.rpart.Y
## 1 11
## 2 40
## Reference
## Prediction N Y
## N 490 57
## Y 11 40
## responsive.fctr responsive.fctr.predict.Final.rpart.N
## 1 N 490
## 2 Y 57
## responsive.fctr.predict.Final.rpart.Y
## 1 11
## 2 40
## Reference
## Prediction N Y
## N 490 57
## Y 11 40
## responsive.fctr responsive.fctr.predict.Final.rpart.N
## 1 N 490
## 2 Y 57
## responsive.fctr.predict.Final.rpart.Y
## 1 11
## 2 40
## Reference
## Prediction N Y
## N 501 97
## Y 0 0
## responsive.fctr responsive.fctr.predict.Final.rpart.N
## 1 N 501
## 2 Y 97
## responsive.fctr.predict.Final.rpart.Y
## 1 0
## 2 0
## Reference
## Prediction N Y
## N 501 97
## Y 0 0
## responsive.fctr responsive.fctr.predict.Final.rpart.N
## 1 N 501
## 2 Y 97
## responsive.fctr.predict.Final.rpart.Y
## 1 0
## 2 0
## Reference
## Prediction N Y
## N 501 97
## Y 0 0
## responsive.fctr responsive.fctr.predict.Final.rpart.N
## 1 N 501
## 2 Y 97
## responsive.fctr.predict.Final.rpart.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2791367
## 2 0.1 0.2791367
## 3 0.2 0.5405405
## 4 0.3 0.5405405
## 5 0.4 0.5405405
## 6 0.5 0.5405405
## 7 0.6 0.5405405
## 8 0.7 0.5405405
## 9 0.8 0.0000000
## 10 0.9 0.0000000
## 11 1.0 0.0000000## [1] "Classifier Probability Threshold: 0.7000 to maximize f.score.fit"
## responsive.fctr responsive.fctr.predict.Final.rpart.N
## 1 N 490
## 2 Y 57
## responsive.fctr.predict.Final.rpart.Y
## 1 11
## 2 40
## Reference
## Prediction N Y
## N 490 57
## Y 11 40
## responsive.fctr responsive.fctr.predict.Final.rpart.N
## 1 N 490
## 2 Y 57
## responsive.fctr.predict.Final.rpart.Y
## 1 11
## 2 40
## Prediction
## Reference N Y
## N 490 11
## Y 57 40
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.862876e-01 4.827121e-01 8.580773e-01 9.106084e-01 8.377926e-01
## AccuracyPValue McnemarPValue
## 4.959708e-04 4.841058e-08## Warning in mypredict_mdl(mdl, df = fit_df, rsp_var, rsp_var_out,
## model_id_method, : Expecting 1 metric: Accuracy; recd: Accuracy, Kappa;
## retaining Accuracy only
## model_id model_method
## 1 Final.rpart rpart
## feats
## 1 california, price, electr, state, demand, independ, generat, suppli, emerg, summer, ferc, southern, util, custom, dow, high, oper, transmiss, natur, power, iso, jone, forc, gas, longer, capac, low, increas, expect, problem, consum, pay, system, plant, take, higher, rate, feder, said, continu, cap, commiss, sourc, solut, three, among, right, addit, order, author, line, energi, bring, push, major, load, told, can, lower, public, pipelin, peak, cost, supplier, without, copyright, hour, interconnect, econom, two, implement, analyst, june, million, level, close, establish, want, within, purchas, other, part, announc, enough, servic, market, inc, grid, provid, competit, reduc, regulatori, news, steffesnaenronenron, creat, propos, still, situat, profit, X2000, whether, wholesal, transport, paid, fall, action, serv, rais, fuel, technolog, avail, will, today, full, unit, face, pass, compani, requir, cut, point, step, even, andor, earlier, design, past, result, pacif, sell, mani, reason, also, reserv, one, nation, time, consid, billion, percent, deregul, buy, industri, money, key, presid, day, set, remain, issu, success, plan, edison, call, accord, last, sever, own, earli, spot, includ, year, appear, bid, base, balanc, get, staff, anoth, deliveri, sinc, got, control, allow, now, resourc, jeff, small, articl, charg, per, washington, dollar, better, seller, contract, benefit, board, larg, wednesday, help, near, sold, term, turn, construct, five, much, like, around, amount, direct, period, top, structur, offici, governor, signific, meet, rather, end, friday, report, may, fact, dont, agenc, offer, use, follow, file, taken, purpos, opinion, talk, certain, grow, good, keep, altern, busi, dasovichnaenron, exist, come, make, real, drive, avoid, pge, monday, effort, effici, month, back, repres, measur, davi, determin, exchang, develop, lead, need, januari, payment, caus, immedi, least, run, explain, messag, havent, next., tuesday, occur, san, receiv, program, upon, start, refer, ago, prohibit, approxim, although, differ, releas, legisl, analysi, just, later, open, chairman, region, move, clear, administr, forward, post, director, decemb, produc, believ, estim, alreadi, fix, found, recent, tri, consult, facil, particip, trader, seek, see, novemb, crisi, cpuc, richard, far, entir, investig, polici, retail, indic, free, execut, respons, materi, modifi, X100, citi, X2001, steven, new, third, cover, oblig, long, portion, august, suggest, basi, idea, return, possibl, tariff, procedur, might, person, regul, readi, condit, agre, though, due, four, combin, X1999, big, someth, comput, look, averag, replac, everi, member, press, tom, record, applic, focus, way, hold, particular, short, decis, becom, jame, connect, corp, posit, thing, coupl, howev, court, manag, potenti, littl, juli, thursday, made, put, detail, begin, answer, site, well, must, total, excess, effect, enter, involv, complet, chanc, togeth, negoti, therefor, advanc, notic, ensur, brian, except, home, morn, give, act, storag, accept, coordin, unless, confer, otherwis, privat, futur, publish, share, fail, web, inform, given, arrang, that, statement, project, soon, current, second, question, first, week, william, ask, commod, say, hope, schedul, intend, jim, reach, less, case, outsid, daili, section, instead, name, place, rule, guarante, address, govern, associ, practic, subject, lot, access, trade, locat, susan, model, invest, main, build, depart, financ, note, left, identifi, internet, reflect, actual, express, bill, general, support, import, qualiti, confidenti, view, show, comment, peopl, eric, correct, work, similar, assum, fund, dasovich, late, sign, seem, seen, cash, option, tell, add, word, commerci, flow, sale, limit, attorney, previous, concern, submit, karen, approach, data, necessari, decid, various, recommend, abl, along, water, physic, claim, final, error, number, event, think, paul, alan, april, parti, exampl, els, group, anyon, dissemin, distribut, onlin, realli, south, delet, improv, sue, law, individu, oil, prepar, hear, leav, advis, head, standard, cynthia, joe, notifi, speak, thought, jan, tim, know, awar, strong, page, mean, sender, activ, organ, east, present, protect, corpor, west, kevin, enron, initi, singl, area, relat, kind, everyon, firm, electron, extend, assist, affect, octob, origin, specif, attent, septemb, deliv, chang, york, rob, hard, north, valu, termin, capit, offic, either, calcul, yet, ill, consider, side, approv, request, shall, asset, product, feel, let, carol, someon, march, probabl, sent, commit, appropri, matter, depend, research, chris, keannaenronenron, quick, contain, hand, gari, respect, steve, guy, date, deal, tomorrow, repli, latest, interest, extens, scott, financi, liquid, texa, local, intern, entiti, vinc, anyth, team, read, great, via, risk, impact, proceed, desk, ive, done, harri, llc, afternoon, mari, michael, cours, best, perform, link, delay, account, committe, opportun, privileg, process, find, sure, mail, greg, send, legal, recipi, london, transact, confirm, mention, summari, convers, prior, visit, respond, bit, yesterday, websit, format, review, paper, georg, peter, handl, kelli, item, employe, net, provis, februari, suit, frank, robert, attend, strategi, dave, email.1, street, join, dan, list, ken, met, appreci, draft, contact, status, bob, version, type, appli, sorri, mark, credit, understand, kaminskihouect, etc, rick, brief, communic, document, bank, sheet, phone, gerald, ena, copi, fyi, counsel, john, updat, jeffrey, roger, lesli, lisa, agreement, volum, letter, elizabeth, settlement, discuss, houston, global, info, transfer, pleas, america, europ, smith, stephani, memo, dear, regard, fax, form, counterparti, languag, mike, master, X77002, david, check, print, amend, eol, kate, attach, book, X1400, revis, thank
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 5.008 1.146
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.6952075 0.7 0.5405405 0.8812563
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8580773 0.9106084 0.4641375
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.02384723 0.1330873glb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="fit.data.training.all",
chunk_step_major=glb_script_df[nrow(glb_script_df), "chunk_step_major"],
chunk_step_minor=glb_script_df[nrow(glb_script_df), "chunk_step_minor"]+1,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))## chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed10 fit.data.training.all 6 0 980.008
## elapsed11 fit.data.training.all 6 1 994.875glb_rsp_var_out <- paste0(glb_rsp_var_out, tail(names(glb_models_lst), 1))
# Used again in predict.data.new chunk
glb_get_predictions <- function(df) {
if (glb_is_regression) {
df[, glb_rsp_var_out] <- predict(glb_fin_mdl, newdata=df, type="raw")
print(myplot_scatter(df, glb_rsp_var, glb_rsp_var_out,
smooth=TRUE))
df[, paste0(glb_rsp_var_out, ".err")] <-
abs(df[, glb_rsp_var_out] - df[, glb_rsp_var])
print(head(orderBy(reformulate(c("-", paste0(glb_rsp_var_out, ".err"))),
df)))
}
if (glb_is_classification && glb_is_binomial) {
# incorporate glb_clf_proba_threshold
# shd it only be for glb_fin_mdl or for earlier models ?
if (glb_models_df[glb_models_df$model_id == glb_fin_mdl_id,
"opt.prob.threshold.fit"] !=
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.fit"])
stop("user specification for probability threshold required")
else prob_threshold <-
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id, "opt.prob.threshold.OOB"]
df[, paste0(glb_rsp_var_out, ".prob")] <-
predict(glb_fin_mdl, newdata=df, type="prob")[, 2]
df[, glb_rsp_var_out] <-
factor(levels(df[, glb_rsp_var])[
(df[, paste0(glb_rsp_var_out, ".prob")] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# prediction stats already reported by myfit_mdl ???
}
if (glb_is_classification && !glb_is_binomial) {
df[, glb_rsp_var_out] <- predict(glb_fin_mdl, newdata=df, type="raw")
}
return(df)
}
glb_trnent_df <- glb_get_predictions(df=glb_trnent_df)
print(glb_feats_df <- mymerge_feats_importance(feats_df=glb_feats_df, sel_mdl=glb_fin_mdl,
entity_df=glb_trnent_df))## id cor.y exclude.as.feat cor.y.abs
## 97 california 0.2846784134 0 0.2846784134
## 543 price 0.3618825045 0 0.3618825045
## 220 electr 0.2274094320 0 0.2274094320
## 677 state 0.2270107697 0 0.2270107697
## 188 demand 0.3297669856 0 0.3297669856
## 2 abl 0.0845103278 0 0.0845103278
## 3 accept 0.1129420132 0 0.1129420132
## 4 access 0.1001766721 0 0.1001766721
## 5 accord 0.1878658336 0 0.1878658336
## 6 account 0.0265246912 0 0.0265246912
## 7 act 0.1133705696 0 0.1133705696
## 8 action 0.2079681852 0 0.2079681852
## 9 activ 0.0658970649 0 0.0658970649
## 10 actual 0.0968299975 0 0.0968299975
## 11 add 0.0896725857 0 0.0896725857
## 12 addit 0.2427842871 0 0.2427842871
## 13 address 0.1023573041 0 0.1023573041
## 14 administr 0.1436463483 0 0.1436463483
## 15 advanc 0.1154527842 0 0.1154527842
## 16 advis 0.0720170955 0 0.0720170955
## 17 affect 0.0597367710 0 0.0597367710
## 18 afternoon 0.0293898025 0 0.0293898025
## 19 agenc 0.1663038669 0 0.1663038669
## 20 ago 0.1494947820 0 0.1494947820
## 21 agre 0.1276678428 0 0.1276678428
## 22 agreement -0.0209354092 0 0.0209354092
## 23 alan 0.0787157208 0 0.0787157208
## 24 allow 0.1798756043 0 0.1798756043
## 25 along 0.0826894388 0 0.0826894388
## 26 alreadi 0.1410233595 0 0.1410233595
## 27 also 0.1949052432 0 0.1949052432
## 28 altern 0.1635639893 0 0.1635639893
## 29 although 0.1485232829 0 0.1485232829
## 30 amend -0.0546989206 0 0.0546989206
## 31 america -0.0310873866 0 0.0310873866
## 32 among 0.2446311268 0 0.2446311268
## 33 amount 0.1708830127 0 0.1708830127
## 34 analysi 0.1471947144 0 0.1471947144
## 35 analyst 0.2250645423 0 0.2250645423
## 36 andor 0.1983043848 0 0.1983043848
## 38 announc 0.2182869183 0 0.2182869183
## 39 anoth 0.1815550025 0 0.1815550025
## 40 answer 0.1184306734 0 0.1184306734
## 41 anyon 0.0774112768 0 0.0774112768
## 42 anyth 0.0333126394 0 0.0333126394
## 43 appear 0.1847178563 0 0.1847178563
## 44 appli -0.0059544775 0 0.0059544775
## 45 applic 0.1253915176 0 0.1253915176
## 46 appreci -0.0005628301 0 0.0005628301
## 47 approach 0.0865847422 0 0.0865847422
## 48 appropri 0.0467062667 0 0.0467062667
## 49 approv 0.0517967139 0 0.0517967139
## 50 approxim 0.1485690318 0 0.1485690318
## 51 april 0.0784422662 0 0.0784422662
## 52 area 0.0618636151 0 0.0618636151
## 53 around 0.1710622811 0 0.1710622811
## 54 arrang 0.1103288539 0 0.1103288539
## 55 articl 0.1773456393 0 0.1773456393
## 56 ask 0.1073844240 0 0.1073844240
## 57 asset 0.0506077006 0 0.0506077006
## 58 assist 0.0606951685 0 0.0606951685
## 59 associ 0.1014997755 0 0.1014997755
## 60 assum 0.0923432579 0 0.0923432579
## 61 attach -0.0568613498 0 0.0568613498
## 62 attend 0.0048625691 0 0.0048625691
## 63 attent 0.0584754134 0 0.0584754134
## 64 attorney 0.0871924819 0 0.0871924819
## 65 august 0.1310950534 0 0.1310950534
## 66 author 0.2409912244 0 0.2409912244
## 67 avail 0.2056318732 0 0.2056318732
## 68 averag 0.1264408658 0 0.1264408658
## 69 avoid 0.1594811790 0 0.1594811790
## 70 awar 0.0681569147 0 0.0681569147
## 71 back 0.1577318831 0 0.1577318831
## 72 balanc 0.1826817517 0 0.1826817517
## 73 bank -0.0120254810 0 0.0120254810
## 74 base 0.1833245518 0 0.1833245518
## 75 basi 0.1309215202 0 0.1309215202
## 76 becom 0.1242550168 0 0.1242550168
## 77 begin 0.1185287292 0 0.1185287292
## 78 believ 0.1420843002 0 0.1420843002
## 79 benefit 0.1744341198 0 0.1744341198
## 80 best 0.0288031434 0 0.0288031434
## 81 better 0.1750624183 0 0.1750624183
## 82 bid 0.1838604673 0 0.1838604673
## 83 big 0.1270473542 0 0.1270473542
## 84 bill 0.0964474347 0 0.0964474347
## 85 billion 0.1938001528 0 0.1938001528
## 86 bit 0.0154817094 0 0.0154817094
## 87 board 0.1743414318 0 0.1743414318
## 88 bob -0.0041026714 0 0.0041026714
## 89 book -0.0609142466 0 0.0609142466
## 90 brian 0.1144734972 0 0.1144734972
## 91 brief -0.0099499211 0 0.0099499211
## 92 bring 0.2377341515 0 0.2377341515
## 93 build 0.0989503634 0 0.0989503634
## 94 busi 0.1618432907 0 0.1618432907
## 95 buy 0.1925542965 0 0.1925542965
## 96 calcul 0.0526493593 0 0.0526493593
## 98 call 0.1879236680 0 0.1879236680
## 99 can 0.2326877882 0 0.2326877882
## 100 cap 0.2503576934 0 0.2503576934
## 101 capac 0.2682464816 0 0.2682464816
## 102 capit 0.0538705276 0 0.0538705276
## 103 carol 0.0502871627 0 0.0502871627
## 104 case 0.1053131128 0 0.1053131128
## 105 cash 0.0904710607 0 0.0904710607
## 106 caus 0.1530010652 0 0.1530010652
## 107 certain 0.1642331018 0 0.1642331018
## 108 chairman 0.1453714591 0 0.1453714591
## 109 chanc 0.1164374481 0 0.1164374481
## 110 chang 0.0574315256 0 0.0574315256
## 111 charg 0.1772441050 0 0.1772441050
## 112 check -0.0541349233 0 0.0541349233
## 113 chris 0.0456306353 0 0.0456306353
## 114 citi 0.1336268117 0 0.1336268117
## 115 claim 0.0807954025 0 0.0807954025
## 116 clear 0.1438512387 0 0.1438512387
## 117 close 0.2232701830 0 0.2232701830
## 118 combin 0.1272186050 0 0.1272186050
## 119 come 0.1613047724 0 0.1613047724
## 120 comment 0.0939611357 0 0.0939611357
## 121 commerci 0.0894652593 0 0.0894652593
## 122 commiss 0.2476310885 0 0.2476310885
## 123 commit 0.0482834163 0 0.0482834163
## 124 committe 0.0264524308 0 0.0264524308
## 125 commod 0.1071883659 0 0.1071883659
## 126 communic -0.0101152504 0 0.0101152504
## 127 compani 0.2015851458 0 0.2015851458
## 128 competit 0.2143873820 0 0.2143873820
## 129 complet 0.1165946938 0 0.1165946938
## 130 comput 0.1266325207 0 0.1266325207
## 131 concern 0.0870677550 0 0.0870677550
## 132 condit 0.1277129624 0 0.1277129624
## 133 confer 0.1127024670 0 0.1127024670
## 134 confidenti 0.0945909102 0 0.0945909102
## 135 confirm 0.0209729992 0 0.0209729992
## 136 connect 0.1237597425 0 0.1237597425
## 137 consid 0.1938138386 0 0.1938138386
## 138 consider 0.0524228791 0 0.0524228791
## 139 construct 0.1723352395 0 0.1723352395
## 140 consult 0.1399908689 0 0.1399908689
## 141 consum 0.2613087272 0 0.2613087272
## 142 contact -0.0024545949 0 0.0024545949
## 143 contain 0.0446346826 0 0.0446346826
## 144 continu 0.2508018743 0 0.2508018743
## 145 contract 0.1746950048 0 0.1746950048
## 146 control 0.1801757972 0 0.1801757972
## 147 convers 0.0191495933 0 0.0191495933
## 148 coordin 0.1128309122 0 0.1128309122
## 149 copi -0.0178634241 0 0.0178634241
## 150 copyright 0.2262725143 0 0.2262725143
## 151 corp 0.1230191291 0 0.1230191291
## 152 corpor 0.0645460699 0 0.0645460699
## 153 correct 0.0936543483 0 0.0936543483
## 154 cost 0.2299010666 0 0.2299010666
## 155 counsel -0.0181877075 0 0.0181877075
## 156 counterparti -0.0461423791 0 0.0461423791
## 157 coupl 0.1225369275 0 0.1225369275
## 158 cours 0.0288839391 0 0.0288839391
## 159 court 0.1216638983 0 0.1216638983
## 160 cover 0.1323380010 0 0.1323380010
## 161 cpuc 0.1381157700 0 0.1381157700
## 162 creat 0.2111880091 0 0.2111880091
## 163 credit -0.0073698818 0 0.0073698818
## 164 crisi 0.1384501258 0 0.1384501258
## 165 current 0.1090906743 0 0.1090906743
## 166 custom 0.2940276360 0 0.2940276360
## 167 cut 0.2009874514 0 0.2009874514
## 168 cynthia 0.0715587325 0 0.0715587325
## 169 daili 0.1050866173 0 0.1050866173
## 170 dan 0.0010462053 0 0.0010462053
## 171 dasovich 0.0921999807 0 0.0921999807
## 172 dasovichnaenron 0.1614242938 0 0.1614242938
## 173 data 0.0865621627 0 0.0865621627
## 174 date 0.0403359306 0 0.0403359306
## 175 dave 0.0037959924 0 0.0037959924
## 176 davi 0.1562381205 0 0.1562381205
## 177 david -0.0528946310 0 0.0528946310
## 178 day 0.1900533742 0 0.1900533742
## 179 deal 0.0398798722 0 0.0398798722
## 180 dear -0.0416546021 0 0.0416546021
## 181 decemb 0.1423802660 0 0.1423802660
## 182 decid 0.0852910750 0 0.0852910750
## 183 decis 0.1243710838 0 0.1243710838
## 184 delay 0.0267673198 0 0.0267673198
## 185 delet 0.0745784299 0 0.0745784299
## 186 deliv 0.0576299250 0 0.0576299250
## 187 deliveri 0.1815451261 0 0.1815451261
## 189 depart 0.0987739642 0 0.0987739642
## 190 depend 0.0458701667 0 0.0458701667
## 191 deregul 0.1926780215 0 0.1926780215
## 192 design 0.1969664065 0 0.1969664065
## 193 desk 0.0305569005 0 0.0305569005
## 194 detail 0.1191149613 0 0.1191149613
## 195 determin 0.1559586937 0 0.1559586937
## 196 develop 0.1558093848 0 0.1558093848
## 197 differ 0.1481531680 0 0.1481531680
## 198 direct 0.1708063710 0 0.1708063710
## 199 director 0.1424163619 0 0.1424163619
## 200 discuss -0.0236944927 0 0.0236944927
## 201 dissemin 0.0771159461 0 0.0771159461
## 202 distribut 0.0766352136 0 0.0766352136
## 203 document -0.0108489602 0 0.0108489602
## 204 dollar 0.1760740085 0 0.1760740085
## 205 done 0.0300713346 0 0.0300713346
## 206 dont 0.1665355866 0 0.1665355866
## 207 dow 0.2905452391 0 0.2905452391
## 208 draft -0.0021763192 0 0.0021763192
## 209 drive 0.1602984764 0 0.1602984764
## 210 due 0.1273219088 0 0.1273219088
## 211 earli 0.1860802434 0 0.1860802434
## 212 earlier 0.1971131364 0 0.1971131364
## 213 east 0.0653380576 0 0.0653380576
## 214 econom 0.2254009118 0 0.2254009118
## 215 edison 0.1886458114 0 0.1886458114
## 216 effect 0.1166638683 0 0.1166638683
## 217 effici 0.1580716521 0 0.1580716521
## 218 effort 0.1582847606 0 0.1582847606
## 219 either 0.0533418938 0 0.0533418938
## 221 electron 0.0608600186 0 0.0608600186
## 222 elizabeth -0.0227235140 0 0.0227235140
## 223 els 0.0777880196 0 0.0777880196
## 224 email.1 0.0034108360 0 0.0034108360
## 225 emerg 0.3067129504 0 0.3067129504
## 226 employe 0.0101712950 0 0.0101712950
## 227 ena -0.0169622682 0 0.0169622682
## 228 end 0.1670527701 0 0.1670527701
## 229 energi 0.2393082994 0 0.2393082994
## 230 enough 0.2166223912 0 0.2166223912
## 231 enron 0.0631394157 0 0.0631394157
## 232 ensur 0.1151316426 0 0.1151316426
## 233 enter 0.1166464782 0 0.1166464782
## 234 entir 0.1362819129 0 0.1362819129
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## 777 <NA> TRUE FALSE 0.00000
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## 781 smith TRUE FALSE 0.00000
## 782 <NA> TRUE FALSE 0.00000
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## 787 now TRUE FALSE 0.00000
## 788 <NA> TRUE TRUE 0.00000
## 789 <NA> TRUE FALSE 0.00000
## 790 <NA> TRUE FALSE 0.00000
## 1 <NA> TRUE FALSE NA
## 37 <NA> FALSE FALSE NA
## 373 <NA> FALSE FALSE NA
## 608 <NA> NA FALSE NA
## 626 <NA> FALSE FALSE NA
## 708 <NA> FALSE FALSE NA
## 710 <NA> FALSE FALSE NA
## 785 <NA> FALSE FALSE NA# Used again in predict.data.new chunk
glb_analytics_diag_plots <- function(obs_df) {
for (var in subset(glb_feats_df, importance > 0)$id) {
plot_df <- melt(obs_df, id.vars=var,
measure.vars=c(glb_rsp_var, glb_rsp_var_out))
# if (var == "<feat_name>") print(myplot_scatter(plot_df, var, "value",
# facet_colcol_name="variable") +
# geom_vline(xintercept=<divider_val>, linetype="dotted")) else
print(myplot_scatter(plot_df, var, "value", colorcol_name="variable",
facet_colcol_name="variable", jitter=TRUE) +
guides(color=FALSE))
}
if (glb_is_regression) {
# plot_vars_df <- subset(glb_feats_df, importance >
# glb_feats_df[glb_feats_df$id == ".rnorm", "importance"])
plot_vars_df <- orderBy(~ -importance, glb_feats_df)
if (nrow(plot_vars_df) == 0)
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(plot_vars_df) > 1, plot_vars_df$id[2],
".rownames"),
feat_y=plot_vars_df$id[1],
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
id_vars=glb_id_vars)
# + facet_wrap(reformulate(plot_vars_df$id[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
if (nrow(plot_vars_df <- subset(glb_feats_df, importance > 0)) == 0)
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df=obs_df,
feat_x=ifelse(nrow(plot_vars_df) > 1, plot_vars_df$id[2],
".rownames"),
feat_y=plot_vars_df$id[1],
rsp_var=glb_rsp_var,
rsp_var_out=glb_rsp_var_out,
id_vars=glb_id_vars)
# + geom_hline(yintercept=<divider_val>, linetype = "dotted")
)
}
}
glb_analytics_diag_plots(obs_df=glb_trnent_df)
## email
## 1 North America's integrated electricity market requires cooperation on environmental policies Commission for Environmental Cooperation releases working paper on North America's electricity market Montreal, 27 November 2001 -- The North American Commission for Environmental Cooperation (CEC) is releasing a working paper highlighting the trend towards increasing trade, competition and cross-border investment in electricity between Canada, Mexico and the United States. It is hoped that the working paper, Environmental Challenges and Opportunities in the Evolving North American Electricity Market, will stimulate public discussion around a CEC symposium of the same title about the need to coordinate environmental policies trinationally as a North America-wide electricity market develops. The CEC symposium will take place in San Diego on 29-30 November, and will bring together leading experts from industry, academia, NGOs and the governments of Canada, Mexico and the United States to consider the impact of the evolving continental electricity market on human health and the environment. "Our goal [with the working paper and the symposium] is to highlight key environmental issues that must be addressed as the electricity markets in North America become more and more integrated," said Janine Ferretti, executive director of the CEC. "We want to stimulate discussion around the important policy questions being raised so that countries can cooperate in their approach to energy and the environment." The CEC, an international organization created under an environmental side agreement to NAFTA known as the North American Agreement on Environmental Cooperation, was established to address regional environmental concerns, help prevent potential trade and environmental conflicts, and promote the effective enforcement of environmental law. The CEC Secretariat believes that greater North American cooperation on environmental policies regarding the continental electricity market is necessary to: * protect air quality and mitigate climate change, * minimize the possibility of environment-based trade disputes, * ensure a dependable supply of reasonably priced electricity across North America * avoid creation of pollution havens, and * ensure local and national environmental measures remain effective. The Changing Market The working paper profiles the rapid changing North American electricity market. For example, in 2001, the US is projected to export 13.1 thousand gigawatt-hours (GWh) of electricity to Canada and Mexico. By 2007, this number is projected to grow to 16.9 thousand GWh of electricity. "Over the past few decades, the North American electricity market has developed into a complex array of cross-border transactions and relationships," said Phil Sharp, former US congressman and chairman of the CEC's Electricity Advisory Board. "We need to achieve this new level of cooperation in our environmental approaches as well." The Environmental Profile of the Electricity Sector The electricity sector is the single largest source of nationally reported toxins in the United States and Canada and a large source in Mexico. In the US, the electricity sector emits approximately 25 percent of all NOx emissions, roughly 35 percent of all CO2 emissions, 25 percent of all mercury emissions and almost 70 percent of SO2 emissions. These emissions have a large impact on airsheds, watersheds and migratory species corridors that are often shared between the three North American countries. "We want to discuss the possible outcomes from greater efforts to coordinate federal, state or provincial environmental laws and policies that relate to the electricity sector," said Ferretti. "How can we develop more compatible environmental approaches to help make domestic environmental policies more effective?" The Effects of an Integrated Electricity Market One key issue raised in the paper is the effect of market integration on the competitiveness of particular fuels such as coal, natural gas or renewables. Fuel choice largely determines environmental impacts from a specific facility, along with pollution control technologies, performance standards and regulations. The paper highlights other impacts of a highly competitive market as well. For example, concerns about so called "pollution havens" arise when significant differences in environmental laws or enforcement practices induce power companies to locate their operations in jurisdictions with lower standards. "The CEC Secretariat is exploring what additional environmental policies will work in this restructured market and how these policies can be adapted to ensure that they enhance competitiveness and benefit the entire region," said Sharp. Because trade rules and policy measures directly influence the variables that drive a successfully integrated North American electricity market, the working paper also addresses fuel choice, technology, pollution control strategies and subsidies. The CEC will use the information gathered during the discussion period to develop a final report that will be submitted to the Council in early 2002. For more information or to view the live video webcast of the symposium, please go to: http://www.cec.org/electricity. You may download the working paper and other supporting documents from: http://www.cec.org/programs_projects/other_initiatives/electricity/docs.cfm?varlan=english. Commission for Environmental Cooperation 393, rue St-Jacques Ouest, Bureau 200 Montréal (Québec) Canada H2Y 1N9 Tel: (514) 350-4300; Fax: (514) 350-4314 E-mail: info@ccemtl.org ***********
## 2 Jim and Margaret: Attached you will find the latest version of the transition document. It incoprporates most of the suggestions you made as well as the agreements we camr to on my last visit to HOU. Margaret, as you will see I need you help with a couple of pages. Jim, I need you to help me get some people to participate in the Addressing Specific Issues chapter. I hope you find it useful. Best, Ricardo ***********
## 31 ----- Forwarded by Elizabeth Sager/HOU/ECT on 09/11/2000 08:44 AM ----- Kevin M Presto 09/07/2000 10:04 AM To: Elizabeth Sager/HOU/ECT@ECT cc: Subject: Re: Meeting at Dynegy office on Sept 12th, 2:30 to 5:30 Do we need a pre-meeting? Quite honestly, Dynegy's position is not unreasonable - it forces the sinking utility to assume the transmission risk. I understand the EEI position and long term it is definitely the right answer from a liquidity and MTM accounting standpoint and I will clearly support that position in our discussions with 3rd parties. The ultimate goal for ENA is to go away from Into products completely and trade power within zones of RTO's or ISO's where transmission and congestion is managed by an independent operator with all transmission costs passed on to the load. ***********
## 46 Carl has been working with Enron and others and the ISO to develop a model that works for CA as well as for the Desert Southwest (which the ISO would then operate). I think this summary explains the kinds of things were trying to get in congestion management reform. I'll send a few other things to look at. ---------------------- Forwarded by Susan J Mara/SFO/EES on 05/16/2000 10:28 AM --------------------------- Carl Imparato 04/24/2000 12:49:26 PM Sent by: Carl Imparato To: zalaywan@caiso.com cc: smara@enron.com, curt.hatton@gen.pge.com, jim.filippi@gen.pge.com, gackerman@wptf.org, alexp@eccointl.com, kewh@dynegy.com, skelly@iepa.com, jstremel@apx.com, bmspeckman@aol.com Subject: CAISO Congestion Model Ziad: Per our conversation this morning, attached is a summary of what I view to be the key attributes of a "reformed" zonal congestion model. The document does not fully address all of the issues discussed at last Thursday's congestion reform meeting in Sacramento, but I believe that (other than for some details) it is consistent with what both the ISO and many of the market participants are proposing. This summary does not necessarily reflect the views of my clients, who haven't yet had the time to review it... but I don't believe that it would be too far off. I am sending this summary to you to put into context the many comments that I offered at last Thursday's meeting and also to support my view that, if the ISO were to implement the CONG/ASM integration by DECENTRALIZING the process rather than CENTRALIZING the process, there would not be much difference between what I've been advocating in the Southwest and the CAISO's model. (The primary remaining differences would be: (i) the way the "hour-ahead" process works - i.e., continuously vs. one discrete time; and (ii) the way scheduling is done - i.e., the ISO would not act as the SC's representative in acquiring rights that could be made available through inter-zonal counterflows since the SCs would do this themselves.) So there is a real possibility that, with some agreement on the ISO's longer-term plans (whether integration of transmission rights and ancillary services procurement will rely on decentralization vs. centralization), we could bring together the models for the region. Carl [Sue, Curt, Jim, Gary, Alex, Kent, Steven, John and Barney: I'd appreciate any feedback... but if you want me to see it, be sure to send it to cfi1@tca-us.com, NOT the enron address from which this e-mail was sent. Carl] ***********
## 79 I have talked with the San Juan team and all are aware of the upcoming meeting. The team leader is Tracy Kuehl but Chris Gaston, Jeff Grieder, Tom Murphy, Blackie Foutz who are on the team can all help with this project. I will be out of the office until Feb. 5, 2001 but I can be reached by pager, 877-726-7034, or cell phone, 713-304-1021. My pager has the capability of accepting alpha messages. You can send them through Lotus Notes with the address of 8777267034@skytel.com. Do not enter a subject. Just type in the message area. Please include you name at the end so I will know who it's from. If you can set up a meeting after Feb. 5 (preferably Feb. 8 or 9) I would appreciate it so I can attend. Ron M. Jeffery Fawcett 01/24/2001 05:39 PM To:\tRonald Matthews/ET&S/Enron@ENRON cc:\tEric Faucheaux/ET&S/Enron@Enron, Rich Jolly/ET&S/Enron@ENRON, Rick Smith/ET&S/Enron@ENRON, Team San-Juan/ET&S/Enron@ENRON, Steven Harris/ET&S/Enron@ENRON, Kevin Hyatt/ET&S/Enron@Enron, Drew Fossum/ET&S/Enron@ENRON, Mary Kay Miller/ET&S/Enron@ENRON, Susan Scott/ET&S/Enron@ENRON Subject:\tAmeramex Project- Bloomfield Rick Moore with Ameramex called and said their project is moving forward- on a fairly expedited basis. They've recently obtained a City of Farmington pledge of public funds (a precursor to a formal bond issue) to underpin the financing. To refresh your memory, this project is another "trigeneration" project. Ameramex will use the waste steam from the electric generation turbines to heat a hydroponic lettuce farm, as well as use the combustion carbon dioxide. We've previously provided a route map and estimate (0.75 mi. of 4" pipe, $486K direct/indirect cost) to Ameramex. Ameramex's financing deal is now dependent upon completion of the interconnect and commencement of service in approx. 7 months. To this end, Ameramax has requested a walk through of the potential route and a revised (if necessary) estimate of the cost to interconnect. There are several other issues to be dealt with here, including what is the final deal structure; including whether TW may possibly providing interim financing until the issuance of the muni bonds. Rick Moore would like to get together with myself and representatives of operations and/or engineering to survey the route and decide on the ultimate design/configuration of facilities. Depending on the outcome of the survey, he'd like a formal proposal for TW for financing the construction of the lateral and interconnect facilities. Ron, since you provided all the prior facility planning support, I'd like for you to continue providing the liason support to TW Commercial on this project. I'll also be calling on the San Juan Team to work with you and engineering to optimize the design/routing of this interconnect/lateral pipeline. I'll work with the customer to set-up a site meeting when you return to work after 2/5/01. To bring you and others up to speed, I've included a couple of the original email(s) giving a little more detail on the estimate and a map of the route: Ronald Matthews 06/16/2000 03:32 PM To:\tJeffery Fawcett/ET&S/Enron@ENRON cc:\tTerry Galassini/ET&S/Enron@ENRON, Rich Jolly/ET&S/Enron@ENRON, David Roensch/ET&S/Enron@ENRON, Team San-Juan/ET&S/Enron@ENRON Subject:\tAmerimex Energy Farms Project Jeff, I talked with the San Juan team and came up with a pipeline route to the proposed Energy Farms site located in S1/2 SE1/4 Sec. 13, T29N, R11W, and the NE1/2 NE1/4 Sec. 24, T29N, R11W all in San Juan County, NM. Per the correspondence, Energy Farms is looking at a delivery around 15 MMcf/d. The best approach to serving this load would be to take gas from the suction side of Bloomfield CS. The 4" pipeline would start there a travel about 0.75 miles to the northwest section of the proposed site (see attached map). A metering facility would be located at the beginning of the pipeline still inside of TW's property along with the other meter facilities. Below is a breakdown of the facility cost. Estimated costs are + 30% and would have to be refined. 0.75 miles of 4" pipeline & side valve\t\t$286,500 4" orifice meter run w/ gallagher flow \t\t$199,900 conditioner, & flow control $486,400 There is a possibility that the meter facility costs could be lower by using an existing metering header system but a more detailed review would be required. Please feel free to call if you have any questions. I will send you a copy of the estimated costs for both the pipeline and measurement since they can't be attached. Jeffery Fawcett 04/18/2000 11:37 AM To:\tSteven Harris/ET&S/Enron@ENRON, Kevin Hyatt/ET&S/Enron@Enron, Susan Scott/ET&S/Enron@ENRON, Lorraine Lindberg/ET&S/Enron@ENRON, TK Lohman/ET&S/Enron@ENRON, Christine Stokes/ET&S/Enron@ENRON cc:\tRonald Matthews/ET&S/Enron@ENRON, Terry Galassini/ET&S/Enron@ENRON Subject:\tAmerimex Bloomfield Project Attached see a note I sent to Rick Moore. Rick works for a company called Amerimex. Rick and I worked together in gas supply at EPNG ("the early years"). Amerimex is a developer of "trigeneration" projects. In addition to the subject project for Bloomfield, another one is being considered for Santa Theresa, N.M. If you recall, we were introduced to trigeneration when we were discussing an electric generation plant on the site of a hydroponic tomato greenhouse near Gallup. Trigeneration is an environmentally friendly technology that utilizes the "byproducts" of electric generation. "Cogeneration" refers to the process where waste heat/steam generated during the combustion of gas turbines is sold commercially (usually to manufacturing facilities) for plant purposes. "Trigeneration," on the other hand, refers the process where not only is the waste heat/steam utilized, but also the carbon dioxide. Therefore, trigeneration is a perfect fit for a greenhouse that requires both heat and carbon dioxide for the plants. Amerimex has proposed a hydroponic lettuce greenhouse in the Bloomfield, N.M. area. The facility, to be constructed turnkey by United Technologies, would have a nominal rating of 70 MW, resulting in a gas burn of about 15 MMcf/d. The plant would require gas deliveries at approx. 500 psig. The outtake power would be sold both locally and on the grid using a TriStates 69kv transmission line that is proximate to the site. Given the "politically correct" nature of these facilities, the City of Farmington has agreed to fund construction with a $70MM bond. As you can see, we're in the very earliest stages of conversation. We'll keep the group posted on progress. ---------------------- Forwarded by Jeffery Fawcett/ET&S/Enron on 04/18/2000 10:38 AM --------------------------- Jeffery Fawcett 04/18/2000 10:38 AM To:\trmoore01@elp.rr.com cc:\tRonald Matthews/ET&S/Enron@ENRON Subject:\tAmerimex Bloomfield Project Rick, It was really great visiting with you the other day. It never ceases to amaze me how small a world this is. I hope this project moves forward and we get the opportunity to work together again. Attached for your use is the schematic you spoke with Ron Matthews about. It's not too terribly detailed, so I don't know if it will be useful or not. We have much more detailed images of the Bloomfield area on our alignments, but without a legal description, we're a little blind. I don't know if you've had a chance to get the legal description for the proposed plant site yet or not, but if you have it, would you forward that on to me? We could then get started at not only putting together a rough cost estimate for the tap and meter, but we could begin looking for the best route for a lateral line between the facilities. Look forward to hearing from you soon. ***********
## 82 Stacey, Please find the attached May monthly power testing questions for our meeting on Thursday. As you discussed with Jenn Staton, we will be reviewing the daily DPR's tomorrow morning to determine the May curve shift reports that we would like to obtain. Also, since earnings release has been tentatively scheduled for July 12th, we would like to review the June curve shift reports to date and make a portion of our June request now in order to alleviate some of the burden on your group in July. Let me know if this poses any problems. (See attached file: May Power Questions.doc) If you have any questions, please feel free to call me at x56953. Thank you, Jennifer *******************Internet Email Confidentiality Footer******************* Privileged/Confidential Information may be contained in this message. If you are not the addressee indicated in this message (or responsible for delivery of the message to such person), you may not copy or deliver this message to anyone. In such case, you should destroy this message and kindly notify the sender by reply email. Please advise immediately if you or your employer do not consent to Internet email for messages of this kind. Opinions, conclusions and other information in this message that do not relate to the official business of my firm shall be understood as neither given nor endorsed by it. - May Power Questions.doc ***********
## 98 ----- Forwarded by Richard B Sanders/HOU/ECT on 11/29/2000 11:43 AM ----- Mark Palmer@ENRON 11/29/2000 10:56 AM To: Richard Shapiro/HOU/EES@EES, Steven J Kean/NA/Enron@Enron, Jeff Dasovich/NA/Enron@Enron, Richard B Sanders/HOU/ECT@ECT, Paul Kaufman/PDX/ECT@ECT cc: Subject: RE: FW: ANNOUNCEMENT: Notice of Press Conference today Here's the suit filed against the generators. Reporter says our suit will look the same. Mark ----- Forwarded by Mark Palmer/Corp/Enron on 11/29/2000 10:53 AM ----- "Leopold, Jason" <Jason.Leopold@dowjones.com> 11/29/2000 10:47 AM To: "'Mark.Palmer@enron.com'" <Mark.Palmer@enron.com> cc: Subject: RE: FW: ANNOUNCEMENT: Notice of Press Conference today James C. Krause, Esq., SBN 066478 Ralph B. Kalfayan, Esq., SBN 133464 Patrick N. Keegan, Esq., SBN 167698 Stephen W. Poirier, Esq. SBN 200868 KRAUSE & KALFAYAN 1010 Second Avenue, Suite 1750 San Diego, CA 92101 Tel: (619) 232-0331 Fax: (619) 232-4019 Attorneys for Plaintiff (Additional Counsel on Signature Page) SUPERIOR COURT OF THE STATE OF CALIFORNIA FOR THE COUNTY OF SAN DIEGO PAMELA R. GORDON, on behalf of ) Case No. herself and all others similarly situated, ) ) CLASS ACTION COMPLAINT FOR ) VIOLATIONS OF CALIFORNIA Plaintiff, ) BUSINESS AND PROFESSIONS CODE ) oo16720, ET SEQ. AND oo17200 ET SEQ. vs. ) ANTITRUST and UNFAIR BUSINESS ) PRACTICES RELIANT ENERGY, INC.; SOUTHERN ) COMPANY; NRG ENERGY; DYNEGY, ) INC.; AES CORPORATION; WILLIAMS ) ENERGY; DUKE ENERGY NORTH ) AMERICA; and DOES 1 through 100, ) Inclusive ) ) Defendants. ) ___________________________________ ) TABLE OF CONTENTS I. NATURE OF THE CASE 1 II. VENUE 2 III. PARTIES 2 IV. CLASS ACTION ALLEGATIONS 5 V. COMMON ALLEGATIONS OF FACT 6 A. The California Wholesale Electricity Markets 6 B. Defendants' Improper Acts 8 C. Results of Defendants' Activities 13 FIRST CAUSE OF ACTION 15 Violation of Sections 16720 and 16726 of the California Business and Professions Code Trust In Restraint of Trade or Commerce in Violation of the Antitrust Act SECOND CAUSE OF ACTION 18 For Violations of the Unfair Trade Practices Act Based Upon Violations of the Antitrust Act THIRD CAUSE OF ACTION 19 For Violations of the Unfair Trade Practices Act Based Upon Defendants' Unfair Business Acts and Practices FOURTH CAUSE OF ACTION 20 Violations of the Consumers Legal Remedies Act VI. PRAYER FOR RELIEF 21 Plaintiff hereby alleges on information and belief based upon the investigation made by and through her attorneys, as follows: I. NATURE OF THE CASE 1. This is a class action seeking redress under the law of California for defendants' conduct in the market for wholesale electricity. During the spring and summer of 2000, a group of electricity generators and traders, including defendants, exercising market power, unlawfully manipulated the California wholesale electricity market, resulting in grossly inflated wholesale electricity prices throughout the state and much of the Western United States. Defendants accomplished this result by, inter alia, improperly using confidential real time generator capacity, use, and maintenance data, and transmission system flow data to "game" the wholesale electricity market, by withholding electrical generating capacity from the California Power Exchange's forward markets, by improperly parking power with affiliates in other states which was later resold in California at inflated rates, by scheduling previously unplanned plant outages to coincide with other plants' planned maintenance shutdowns, and by scheduling transmission flows to cause or exacerbate congestion. 2. The improper use of confidential real time data was in violation of the California Tariff by which defendants are authorized to sell wholesale electricity within California. 3. By engaging in said unfair business practices, defendants have directly and severely damaged purchasers of electricity supplied through the California Power Exchange. As noted in the August 30, 2000 San Diego Union Tribune: "The utility bills of all [electric] ratepayers in San Diego are exorbitant. . . . Rates that were 3 cents per kilowatt hour have risen to 19 cents per kilowatt hour in just three months -- nothing short of confiscatory rates." 4. In its November 1, 2000 Order, the Federal Energy Regulatory Commission ("FERC") expressly found that electricity prices in California have been maintained at "unjust and unreasonable" levels during the relevant period, though it determined that it did not have the authority to mandate refunds or other retroactive relief with respect thereto. In its November 22, 2000 response to the November 1st FERC order, the California Public Utilities Commission (CPUC) concluded that due to market manipulation by wholesale energy producers and traders, customers of electricity supplied through the California Power Exchange had been overcharged more than $4 billion dollars during the summer of 2000. In its November 22nd response to the November 1st FERC order, even the California Independent System Operator (CAISO) which manages California's transmission system infrastructure, known as the "power grid", found that the summer 2000 prices resulted from the exercise of intolerable levels of market power by generators and traders. II. VENUE 5. Venue is proper because defendants each transact business in the State of California and because the damages caused by defendants' unlawful manipulation of the price of wholesale electricity occurred in San Diego County. III. PARTIES 6. Plaintiff Pamela R. Gordon is an adult resident living in San Diego, California. Plaintiff Pamela R. Gordon is a retail electricity customer of SDG&E, and has been forced to pay rates for electricity during the Class Period (defined infra) that are unjust and unfair and that have been artificially inflated by defendants' misconduct. Moreover, plaintiff Pamela R. Gordon was born with no lymph nodes in her legs which results in a lack of circulation. Exposure to high temperatures will cause plaintiff Pamela R. Gordon's legs to swell, which can lead to possible severe health consequences. As a result of her condition, plaintiff Pamela R. Gordon must maintain her home at temperatures of not more than 67 degrees at night and 71 degrees during the day. Plaintiff Pamela R. Gordon is unable to terminate her electricity service and has been forced to pay unjust and unfair rates for electricity from SDG&E as a result of defendants' misconduct. Specifically, plaintiff Pamela R. Gordon was charged and/or paid the following rates for electricity by SDG&E during the Class Period: / / / / / / / / / / / / Dates Total Electric Charges 5-26 to 6-27-00 $180.92 6-27 to 7-27-00 $226.67 7-28 to 8-28-00 $311.84 8-28 to 9-27-00 $143.48 9-27 to 10-26-00 $84.71 7. Plaintiff was charged less for electricity per kWh prior to the Class Period and the inception of Defendants' wrongful conduct: Dates Total Electric Charges 3-29 to 4-27-00 $100.50 8. Defendant Reliant Energy (hereinafter "Reliant") is a Houston, Texas based public corporation doing business in the state of California as Reliant Energy Services. It is a generator and trader of wholesale electricity which is ultimately sold to California consumers, including plaintiff and the class, and is a WSCC member. Reliant operates five electricity generating plants in California which supply wholesale electricity to the California Power Exchange. 9. Defendant Southern Company (hereinafter "Southern") is an Atlanta, Georgia based public corporation doing business in the state of California as Southern Energy, Inc. It is a generator and trader of wholesale electricity which is ultimately sold to California consumers, including plaintiff and the class, and is a WSCC member. Southern Energy operates six electricity generating plants in California which supply wholesale electricity to the California Power Exchange. 10. Defendant Dynegy, Inc. (hereinafter "Dynegy") is a Houston, Texas based public corporation doing business in the state of California as Dynegy Marketing and Trade. It is a generator and trader of wholesale electricity which is ultimately sold to California consumers, including plaintiff and the class, and is a WSCC member. Dynegy operates two electricity generating power plants in California which supply wholesale electricity to the California Power Exchange. 11. Defendant NRG Energy, Inc. (hereinafter "NRG") is a Minneapolis, Minnesota based public corporation doing business in the state of California as NRG, a generator and trader of wholesale electricity which is ultimately sold to California consumers, including plaintiff and the class. In partnership with defendant Dynegy, NRG operates eight electricity generating plants in California which supply wholesale electricity to the California Power Exchange. 12. Defendant AES Corporation (hereinafter "AES") is an Arlington, Virginia based public corporation doing business in the state of California as AES Pacific Group, a generator and trader of wholesale electricity which is ultimately sold to California consumers, including plaintiffs and the class, and a WSCC member. AES Pacific Group operates four electricity generating plants in southern California which supply wholesale electricity to the California Power Exchange. 13. Defendant Williams Energy is a Tulsa, Oklahoma based public corporation doing business in the state of California as Williams Energy Marketing and Trading Company, a generator and trader of wholesale electricity which is ultimately sold to California consumers, including plaintiffs and the class, and a WSCC member. Williams Energy Marketing and Trading Company operates three electricity generating plants in southern California which supply wholesale electricity to the California Power Exchange. 14. Defendant Duke Energy North America (hereinafter "Duke Energy") is a Houston, Texas based public corporation doing business in the state of California as Duke Energy Trading and Marketing, LLC, a generator and trader of wholesale electricity which is ultimately sold to California consumers, including plaintiffs and the class, and a WSCC member. Duke Energy operates four electricity generating plants in California which supply wholesale electricity to the California Power Exchange. 15. Defendants DOES 1 through 100, inclusive, are sued herein under fictitious names because their true names are unknown to the Plaintiff. When their true names and capacities are ascertained, Plaintiff will amend this complaint by substituting their true names and capacities herein. Plaintiff is informed and believe, and based thereon alleges, that each of the fictitiously named Defendants is responsible in some manner for the occurrences herein alleged, and thereby proximately caused injuries and damages to Plaintiff, as herein alleged. Further, Plaintiff is informed and believes, and upon such information and belief alleges, that each of the Defendants designated by a fictitious name participated in and/or benefited from the wrongful acts, conduct, and omissions described in this complaint, and that said acts, conduct, and omissions directly and proximately caused injury and damages to Plaintiff and the class as alleged below. IV. CLASS ACTION ALLEGATIONS 16. Plaintiff brings this action pursuant to Section 382 of the Code of Civil Procedure as a class action on behalf of all electricity customers, including the retail customers of San Diego Gas & Electric Company (SDG&E), who purchased electricity supplied through the California Power Exchange during the period from May 22, 2000 through and including the date of trial (hereinafter the "Class Period"). All such persons and entities have paid and/or have been charged prices for electricity that were "unjust and unreasonable" and that were artificially inflated due to defendants' exercise of market power, improper use of confidential information, manipulations, and unlawful actions. Members of the Class are extremely numerous and their joinder would be impracticable. Approximately 1.2 million SDG&E customers are within the class. 17. Common issues of fact and law predominate over individual issues, including: -whether defendants committed unfair business practices by sharing confidential real time data in violation of ISO Tariffs and thereafter "gamed" the market; -whether defendants charged "unjust and unreasonable" prices for electricity during the Class Period or otherwise benefited from said inflated price for electricity; -whether defendants exercised "market power" in the California electricity market during the Class Period; -whether defendants received improper and excess profits which should be refunded and paid to members of the Class. 18. Plaintiff's interests are typical of, and not antagonistic to, the interests of the class. 19. Plaintiff has retained competent counsel experienced in class action and consumer fraud litigation and intends to vigorously prosecute this action. 20. A class action is superior to all other methods for the fair and efficient adjudication of this controversy. The size of the individual damages is small in comparison to the complexity and scope of the defendants' operations and alleged misconduct. A class action is the only method whereby plaintiff and the class can efficiently seek redress and obtain a uniform adjudication of their claims. Plaintiff does not anticipate any difficulty with the management of this action. / / / V. COMMON ALLEGATIONS OF FACT A. The California Wholesale Electricity Markets 21. In 1996 California deregulated its electricity industry and required the State's privately owned utility companies to divest themselves of their generating plants. The purpose of deregulation was to introduce competition into the electricity markets, with the intention of reducing rates for electrical power. In fact, however, certain participants in this recently deregulated industry have, in violation of their operating Tariff, taken advantage of structural flaws in the system and improperly obtained confidential competitor information to artificially inflate prices for electricity, yielding themselves billions of dollars in windfall profits at the expense of California consumers. 22. As part of the deregulation, several new entities were created to facilitate the workings of this new market. California's investor owned utilities, Pacific Gas & Electric (PG&E), Southern California Edison (SCE), and San Diego Gas & Electric (SDG&E) (the"Utilities"), now purchase electricity for their customers from independent generators and traders, including defendants, through a market known as the California Power Exchange (PX). The PX maintains a market for the purchase and sale of wholesale electricity through a variety of forward contracts and real time markets. Under the California deregulatory scheme, the Utilities were, until just recently, required to purchase power through the PX and prohibited from independently contracting for power outside this market. 23. The deregulated market also led to the creation of the California Independent System Operator (CAISO). The CAISO is an "independent" non-profit corporation, though its managing board includes representatives from among the generators and traders, that was created to manage the flow of electricity and ensure reliability along the long distance, high-voltage power lines that make up the bulk of California's transmission system. Approximately 75% of California's electricity is distributed through the CAISO managed "power grid". The CAISO monitors electrical loads (i.e., demand) on an on-going basis and ensures that there is an adequate supply of power to meet that demand. / / / 24. The CAISO accomplishes this by maintaining a Real Time Imbalance Market (the "Real Time Market"). When the CAISO receives bids from suppliers that are insufficient to meet the demand for power, it must accept any bid, which then sets the market-clearing price for that hour. Under the single price auction system used by the CAISO, all sellers of electricity in the Real Time Market automatically receive the market clearing price, which is the (second) highest price paid in the market, even if they were willing to sell and had in fact bid to sell electricity at lower prices. This mechanism allows sophisticated market participants, such as defendants, to game the market by withholding bids in an effort to maximize the clearing price. 25. The Real Time Market was not designed to handle large transactions, but merely to provide a mechanism to correct short term imbalances in supply and demand. Defendants nonetheless pushed a substantial portion of the daily wholesale electricity sales into this market by under bidding capacity to the forward market leaving wholesale customers no alternative place to obtain the power to satisfy their retail customers' demand. 26. The CAISO also manages the Replacement Reserve Market, which is used to balance supply and demand of electricity. Sellers in the Replacement Reserve Market receive a fixed premium for having available capacity. If called upon by the ISO to deliver energy, sellers in the Replacement Reserve Market also receive the Real Time Market price. Thus, if generators know that capacity is short, they have an incentive to withhold supplies from the spot markets and push as much as possible into the Replacement Reserve Market. Access to real time generating information facilitates such "gaming" of the markets by demonstrating, in real time, available competing supply. 27. In times of high demand, the CAISO has the authority to purchase energy from out-of-state sources, as to which there is no operative price cap. The CAISO was forced to do so during the Summer of 2000. California generators and marketers can export electricity to surrounding markets in order to create artificial shortages and drive up prices in the California markets. They can then resell that electricity to the CAISO at inflated and uncapped prices. As the PX Compliance Committee noted in its November 1, 2000 Report ("the PX Compliance Report"): / / / "During periods of high Out-of-Market purchases, when prices are above the Real-Time energy price cap, in-state generators have an incentive to export energy out of California. Surrounding control areas can effectively park that energy for resale to the CAISO Out-of-Market calls and return it to the state." Id. at p. 44. 28. Notably, the summer of 2000 witnessed an approximately 370% increase in exports from the California energy market, despite the very high prices and short supplies that existed in the State. "Market power" is defined by the CAISO as the ability to significantly influence market prices and cause them to vary from competitive levels for a material period of time. Generators and suppliers (including defendants) can exercise market power by physically or economically withholding electricity from the market, moving electricity out of the California markets, and by pricing and bidding their resources in ways that impede the efficiency of the market. 29. Notably, the California energy market consists of a relatively small number of firms, some of which control a substantial fraction of the total generating capacity. The small number of suppliers facilitates the exercise of market power during periods of high demand, even when there is not a true scarcity of available generating capacity. In addition, the relatively inelastic demand for energy further facilitates the exercise of such market power. B. Defendants' Improper Acts 30. Prior to May 22, 2000, the CAISO began supplying the Western Systems Coordinating Council ("WSCC"), an organization consisting of electric industry participants, including defendants, with real time industry data as to electricity generating levels, known as metering data, and transmission system flow data, known as scheduling data, pertaining to individual market participants. Through the WSCC Internet web site, wholesale electricity market participants, including defendants herein, were thereby given access to real time data as to their competitors' actions, although access to such data was forbidden by the ISO Tariff. When requested by various governmental entities, including the California Public Utilities Commission ("CPUC") the WSCC refused access. / / / / / / / / / 31. CAISO/FERC Tariff section 10.2.6 states in relevant part; Meter Data supplied by an ISO metered entity shall be made available by the ISO to the scheduling coordinator representing such ISO metered entity and other authorized users identified in its meter services agreement, but shall not be disclosed to any third party except as otherwise may be required by law, FERC any local regulatory authority or other provision of this ISO Tariff. 32. CAISO/FERC Tariff section 20.3.1 states in relevant part: The ISO shall maintain the confidentiality of all of the documents, data and information provided to it by any Market Participant that are treated as confidential or commercially sensitive under Section 20.3.2; provided, however, that the ISO need not keep confidential: (1) information that is explicitly subject to data exchange through WEnet pursuant to Section 6 of this ISO Tariff; (2) information that the ISO or the Market Participant providing the information is required to disclose pursuant to this ISO Tariff, or applicable regulatory requirements (provided that the ISO shall comply with any applicable limits on such disclosure); or (3) information that becomes available to the public on a non-confidential basis (other than as a result of the ISO's breach of this ISO Tariff). 33. CAISO/FERC Tariff section 20.3.2 states in relevant part: The following information provided to the ISO by Scheduling Coordinators shall be treated by the ISO as confidential: (a) individual bids for Supplemental Energy; (b) individual Adjustment Bids for Congestion Management which are not designated by the scheduling coordinator as available; (c) individual bids for Ancillary Services; (d) transactions between Scheduling Coordinators; (e) individual Generator Outage programs unless a Generator makes a change to its Generator Outage program which causes Congestion in the short term (i.e. one month or less), in which case, the ISO may publish the name of that Generator. 34. CAISO/FERC Tariff section 20.3.2 states in relevant part: No Market Participant shall have the right hereunder to receive from the ISO or to review any documents, data or other information of another Market Participant to the extent such documents, data or information is to be treated as in accordance with Section 20.3.2; provided, however, a market Participant may receive and review any composite documents, data, and other information that may be developed based upon such confidential documents, data, or information, if the composite document does not disclose such confidential data or information relating to an individual Market Participant and provided, however, that the ISO may disclose information as provided for in its bylaws. 35. Part of the CAISO Tariff is the ISO Market Monitoring & Information Protocol (MMIP). The objective of this Protocol (MMIP) is to set forth the workplan and rules under which the ISO will monitor the ISO markets to identify abuses of market power. The MMIP part of the tariff applies to all ISO Market Participants, including Defendants. 36. MMIP Section 2.1.1. states in relevant part: Anomalous market behavior, which is defined as behavior that departs significantly from the normal behavior in competitive markets that do not require continuing regulation or as behavior leading to unusual or unexplained market outcomes. Evidence of such behavior may be derived from a number of circumstances, including: MMIP 2.1.1.1 withholding of generation capacity under circumstances in which it would normally be offered in a competitive market; MMIP 2.1.1.2 unexplained or unusual redeclarations of availability by Generators; MMIP 2.1.1.3 unusual trades or transactions; MMIP 2.1.1.4 pricing and bidding patterns that are inconsistent with prevailing supply and demand conditions, e.g., prices and bids that appear consistently excessive for or otherwise inconsistent with such conditions; and MMIP 2.1.1.5 unusual activity or circumstances relating to imports from or exports to other markets or exchanges. The Market Surveillance Unit shall evaluate, on an ongoing basis, whether the continued or persistent presence of such circumstances indicates the presence of behavior that is designed to or has the potential to distort the operation and efficient functioning of a competitive market, e.g., the strategic withholding and redeclaring of capacity, and whether it indicates the presence and exercise of market power or of other unacceptable practices. 37. MMIP Section 2.1.3 states in relevant part: "Gaming", or taking unfair advantage of the rules and procedures set forth in the PX or ISO Tariffs, Protocols or Activity Rules, or of transmission constraints in periods in which exist substantial Congestion, to the detriment of the efficiency of, and of consumers in, the ISO Markets. "Gaming" may also include taking undue advantage of other conditions that may affect the availability of transmission and generation capacity, such as loop flow, facility outages, level of hydropower output or seasonal limits on energy imports from out-of-state, or actions or behaviors that may otherwise render the system and the ISO markets vulnerable to price manipulation to the detriment of their efficiency. 38. Market participants used their trade organization, the WSCC, to share access to and use real time information, notwithstanding the fact that its publication and use violated the ISO's operating procedures, as set forth in tariffs and protocols that were filed with and approved by FERC, and by adoption made part of the contracts by which defendants were authorized to sell electricity to the PX. Those tariffs and protocols required that such data be kept confidential to prevent gaming the market and not be used by market participants to engage in anti-competitive behavior. 39. It has been reported in the Dow Jones Work. Com Newswire on October 19, 2000, that: "Electricity generators may have used real-time plant activity reports from the state's grid operator to their advantage in California's wholesale electricity market, according to an official with the Western Systems Coordinating Council. * * * * * * * "At issue is real-time information the California Independent System Operator provided the Western systems Coordinating Council, a governmental [sic] organization that monitors electricity reliability in the western U.S., about power plant activity in the state. "The real-time information allows market participants . . . to access data via an internet site that shows how much capacity a plant with more than 200 megawatts has online at any given moment. . . . . "The information was intended to be used to monitor electric reliability on the grid . . . . "Last month, however, the ISO's attorneys alerted the WSCC that the 'data is being used against them and to game the market,' according to Bill Commish, director of dispatch with the WSCC. * * * * * * "Commish said generators could use the information to withhold supply and drive up power prices or to identify transmission congestion in a particular region and use that to gouge customers. "However, the ISO, which controls about 75% of the state's power grid and real-time market, may have violated a FERC rule because it is required to keep such information confidential for 90 days, an ISO attorney told the WSCC. "Beginning Monday, the ISO will no longer provide such information to the WSCC or other market participants." 40. Starting on or about May 22, 2000, Defendants used such real time data to exercise market power by, among other things, reducing their output, strategically under-bidding supply to the forward markets, and exporting electricity from the State in order to drive up the Real Time Market price and other market prices. As a direct result of their improper access to and use of this information, the price of wholesale electricity spiked sharply upwards on May 22, 2000 and has remained at artificially inflated prices ever since. FERC, CPUC, and CAISO Dept. of Market Analysis, have all now concluded that market participants should be excluded from the CAISO board. 41. As noted in the North County Times on October 19, 2000: "A case study of San Diego County's two big power plants has concluded that they held back from full production of electricity in June, even as prices skyrocketed and California's power manager was scrambling for supplies to prevent blackouts. "To the study's author . . . the low production in San Diego county is clear evidence that the companies that generate and trade electricity in CA were creating an artificial shortage to drive up prices. * * * * * * McCullough's hour-by-hour analysis of power output for June found that the Encina plant in Carlsbad generated 44 percent of the megawatt hours that it could have during periods when prices were higher than the plant's cost of production. The South Bay power plant in Chula Vista generated 61 percent of what would be expected under traditional economic theory. Economists estimate it costs $45-$55 per megawatt hour to generate electricity at the plants. The wholesale price in June averaged $120 per megawatt hour. A separate analysis conducted by the North County Times revealed "mysterious cutbacks in Carlsbad during a heady market of sky-high prices." * * ** * "Investigators and market analysts have documented extensive evidence that electricity traders have waited until prices rose on California's market this summer before they would commit their power plants to production." "'We did see evidence of withholding in the bidding,' according to Jim Detmers, the ISO's operations chief." 42. In addition, defendants used such real time information about their competitors to improperly withhold electrical supplies from the forward markets operated by the PX in order to take advantage of the ISO's need to balance supply and demand in the spot market and thereby benefit from the single price auction system by obtaining the highest price paid at any given period. In addition, certain defendants sold or parked electricity with affiliates in other states in order to artificially drive up prices in the California markets. They then sold that electricity back into the California markets at the artificially inflated prices they had created. These tactics forced buyers in the California wholesale electricity market to purchase more than 30% of their electricity needs in the inflated Spot Market, rather than the less than 5% that should be sold in that market. The Spot Market was solely intended to satisfy last minute fluctuations in the demand for energy. C. Results of Defendants' Activities 43. From late May 2000 through the present, defendants' activities have raised wholesale electricity prices to record levels, well above the rates that would prevail in a competitive marketplace and disproportionate to the costs of generating that electricity. Those prices have resulted in an approximate tripling of electricity rates in areas served by SDG&E, where retail rates have been deregulated and represent in large part a pass through of SDG&E's costs for such power, which it purchases from the defendant generators and traders through the California Power Exchange. 44. As the Market Surveillance Committee ("MSC") of the CAISO concluded in its September 6, 2000 Report ("the MSC Report"), during the months of May and June 2000 revenues from the sale of ISO loads in the California energy market were 37% and 182%, respectively, above the revenues that would have been generated under competitive pricing conditions. MSC Report at p. 2. The MSC Report unambiguously concluded that market participants exercised a "significant amount of market power" in the California energy markets, beginning as early as October 1999 and increasing dramatically during the Class Period. Id. at pp. 4, 15, 17. 45. The average market clearing price in California's wholesale markets during August of 2000 was $166.24/MWh under moderate load conditions, compared with $32.31/MWh a year earlier. While load demand conditions in California during August 2000 were slightly higher than in August 1999, they were similar to those during August 1998 when prices were much lower. Peak demand in August 2000 was lower than in August of either of the prior two years, negating representations by industry leaders that excessive demand caused these price increases. Indeed, some California electricity producers were only running at 60% of capacity during the so-called emergency periods. 46. These price hikes have even been effect during the middle of the night, when electricity is abundant and demand is low. Further, prices in November 2000, when demand for electricity is much lower than during the summer months, continue to be substantially in excess of the prior years' levels and in excess of the levels that should prevail in a competitive marketplace. 47. As a result of defendants' exercise of market power, they artificially increased prices to record levels and have obtained huge windfall profits. As reported in the October 17, 2000 San Diego Union Tribune, analyst Anatol Feygin of JP Morgan estimated that electricity industry profits from California for the 3 months ended September 30, 2000 could reach $6 billion, even after taking into account cost increases. Feygin stated, "'The industry was literally at eight times the profitability of last year . . .. that is a fortune.'" 48. Defendant Reliant has reported that its third quarter earnings will top last year's figure by about $110 million. Similarly, defendant Dynegy reported an 83 percent increase in its third quarter 2000 income, as compared to its third quarter 1999 figures. Profits from Defendant Dynegy's wholesale energy generating and trading division quadrupled to $141.9 million, which represented 80 percent of Dynegy's overall profits. "This is the most successful quarter in Dynegy's history", reported chairman and chief executive officer, Charles 'Chuck' Watson. Other defendants also obtained large windfall profits during this period. 49. Prior to deregulation, the historical cost to produce a megawatt hour of electricity in San Diego County was approximately $23-45 per MWh. During the Summer of 2000, defendants' conduct caused the Real Time Market price of wholesale electricity to frequently reach $750 per MWh, resulting in an approximate tripling of SDG&E customers' bills. 50. In its Order issued on November 1, 2000, FERC concluded that wholesale prices of electricity in California had been at "unjust and unreasonable" levels during the Summer of 2000. CAISO and CPUC responses to the November 1st FERC order both concluded that wholesale prices during Summer 2000 were due to a substantial exercise of market power by generators and traders. 51. Plaintiffs and the class were injured by the manipulated and inflated prices paid for wholesale electricity, including retail customers of SDG&E since costs were fully passed through to them. The CPUC's November 22, 2000 report to FERC concluded that the exercise of market power by wholesale producers resulted in a $4 billion dollar overcharge for the delivery of electricity to California during the summer of 2000. Due to the unlawful conduct alleged above, plaintiffs and the class paid approximately $4 billion dollars more for electricity than they otherwise would have, causing great hardship to numerous individuals and businesses. As Congressman Brian Bilbray stated: "Every day that goes by you've got small businesses bleeding to death. People are literally dying financially from this situation." Work.com Newswire (10/21/00). 52. The improper sharing and use of real time wholesale electricity generation and transmission data by defendants to withhold generation by unplanned outages and to maintain bidding strategies which led to contrived and artificial shortages, and other manipulative conduct, had the effect of artificially inflating and maintaining wholesale electricity prices above competitive prices allowing defendants to reap illegal profits amounting to billions of dollars, at the expense of plaintiffs and the class. FIRST CAUSE OF ACTION Violation of Sections 16720 and 16726 of the California Business and Professions Code Trust In Restraint of Trade or Commerce in Violation of the Antitrust Act (Against All Defendants) 53. Plaintiffs reallege and incorporate by reference each and every allegation set forth above. 54. The Cartwright Act states, at o16726, that: "Except as provided in this chapter, every trust is unlawful, against public policy and void." A trust is defined at o16720 as follows: 55. "A trust is a combination of capital, skill or acts by two or more persons for any of the following purposes: (a) To create or carry out restrictions in trade or commerce. (b) To limit or reduce the production, or increase the price of merchandise or of any commodity. (c) To prevent competition in manufacturing, making, transportation, sale or purchase of merchandise, produce or any commodity. (d) To fix at any standard or figure, whereby its price to the public or consumer shall be in any manner controlled or established, any article or commodity of merchandise, produce or commerce intended for sale, barter, use or consumption in this State. (e) To make or enter into or execute or carry out any contracts, obligations, or agreements of any kind or description, by which they do all or any or any combination of the following: (1) Bind themselves not to sell, dispose of or transport any article or any commodity or any article of trade, use, merchandise, commerce or consumption below a common standard figure, or fixed value. (2) Agree in any manner to keep the price of such article, commodity or transportation at a fixed or graduated figure. (3) Establish or settle the price of any article, commodity or transportation between them or themselves and others, so as directly or indirectly to preclude a free and unrestricted competition among themselves, or any purchasers or consumers in the sale or transportation of any such article or commodity. (4) Agree to pool, combine or directly or indirectly unite any interests that they may have connected with the sale or transportation of any such article or commodity, that its price might in any manner be affected." 56. Plaintiffs and the Class are empowered by the Cartwright Act o16750 to commence a private action for up to three times their damages and equitable relief due to the injuries they have suffered and continue to suffer as a result of defendants' violations of the Antitrust Act. The Cartwright Act states: Any person who is injured in his or her business or property by reason of anything forbidden or declared unlawful by this chapter, may sue therefore . . . and to recover three times the damages sustained by him or her, interest . . . and preliminary or permanent injunctive relief . . . . 57. Plaintiffs and the Class, are "persons" within the meaning of the Antitrust Act as defined in o16702. 58. Beginning on or about May 22, 2000, the exact date being presently unknown to Plaintiffs, Defendants embarked on a conspiracy to make available and use confidential real time industry data as to electrical generating, power plant capacity, utilization, and outages through the WSCC, an organization consisting of Defendants, and others, to inflate the market prices of wholesale electricity, in restraint of trade and commerce in California. As a result of these efforts, Defendants engaged in acts and entered into Agreements, which were intended to and have in fact restrained trade and commerce in the California Power Exchange and other electricity markets, in violation of the Antitrust Act, which violations are continuing to the present day. 59. The aforesaid acts, contracts, agreements, combinations and conspiracies in restraint of trade or commerce have consisted of continuing agreements, undertakings and concert of action among Defendants, the substantial terms of which were to artificially limit the supply of electricity to the California markets. Through these acts and Agreements, Defendants acted to set, raise and maintain the prices of electricity on the Real Time Imbalance Market and other markets to wholesale purchasers of electricity, and, indirectly and ultimately, to California consumers, at supra-competitive prices. 60. Defendants successfully set, raised and maintained supra-competitive wholesale prices for electricity beginning on May 22, 2000, to the customers of the California Power Exchange. 61. Such overcharges were paid by consumers of electricity, such as Plaintiff and the Class she seeks to represent. 62. Defendants' acts, contracts, agreements, combinations and conspiracies were intended to restrain and did, in fact, restrain trade and commerce in California during the Class Period. 63. Defendants' unlawful restraints of trade have had and, unless enjoined, threaten to continue to have the following anti-competitive effects, among others: (a) Prices charged by Defendants for electricity to wholesale purchasers, and, ultimately, to the customers of SDG&E in California have been and will continue to be set, raised and maintained at artificially high and non-competitive levels; (b) Fair and equitable price competition for the supply of electricity and retail and wholesale price levels for electricity have been restrained and adversely affected; and (c) Plaintiff and other Class members have been deprived of the benefit of free and open competition in the electricity market in California. 64. As a direct and proximate result of the violations alleged herein, Plaintiff and members of the Class have been unable to and will continue to be unable to purchase electricity at prices determined by free and open competition, and Plaintiff and members of the Class have been damaged and will continue to be damaged by their respective purchases and/or charges for electricity at prices higher than they would have otherwise paid, absent Defendants' unlawful conduct. 65. Plaintiff and the Class have no adequate remedy at law for their irreparable injuries. 66. Defendants' acts, contracts, agreements, conspiracies, and combinations in restraint of trade violate the Antitrust Act. Accordingly, Plaintiff and the Class seek three times their damages caused by Defendants' violations of the Antitrust Act and a permanent injunction enjoining Defendants' continuing violations of the Cartwright Act. / / / SECOND CAUSE OF ACTION For Violations of the Unfair Trade Practices Act Based Upon Violations of the Antitrust Act (against all defendants) 67. Plaintiffs reallege and incorporate by reference each and every allegation set forth above. 68. The Unfair Trade Practices Act prohibits all unfair competition, which is defined as "any unlawful, unfair or fraudulent business act or practice," and includes violations of the Cartwright Act, oo16700 et seq. of the B&P Code. 69. As alleged hereinabove, beginning as early as May, 2000, defendants unlawfully conspired, agreed, arranged and combined to prevent and restrain competition for the sale of electricity, in violation of oo 16700 et seq. and oo 17200 et seq. of the B&P Code. 70. Pursuant to B&P Code oo 17200 et seq., Plaintiff brings this action seeking injunctive relief to enjoin defendants' unfair trade practices and requiring defendants' disgorgement of all monies obtained by virtue of their violations of the Cartwright Act as described hereinabove. 71. As a direct and proximate result of the violations alleged herein, Plaintiff and members of the Class have been unable to and will continue to be unable to purchase electricity at prices determined by free and open competition, and Plaintiff and members of the Class have been damaged and will continue to be damaged by their respective purchases and/or charges for electricity at prices higher than they would have otherwise paid, absent defendants' unlawful conduct. 72. Plaintiff and the Class have no adequate remedy at law for their irreparable injuries. 73. Defendants' acts, contracts, agreements, conspiracies, and combinations in restraint of trade and free and open competition, alleged herein, violate the Antitrust Act. Accordingly, Plaintiff and the Class seek restitution and disgorgement by defendants to the Class of all monies obtained by defendants' acts of unfair competition with respect to the contract, conspiracy, combination and trust, and a permanent injunction enjoining defendants' continuing violations of the Cartwright Act. / / / / / / THIRD CAUSE OF ACTION For Violations of the Unfair Trade Practices Act Based Upon Defendants' Unfair Business Acts and Practices In Charging Unjust and Unreasonable Rates (against all defendants) 74. Plaintiffs reallege and incorporate by reference each and every allegation set forth above. 75. Section 17200 et seq. of the California Business & Professions Code prohibits acts of unfair competition, which include any "unlawful, unfair, or fraudulent business act or practice and unfair, deceptive, untrue or misleading advertising". 76. The Defendants consistently engaged in numerous acts of "unfair competition", as more fully described above, by inter alia, manipulating the price of wholesale electricity in the California markets. 77. In addition, pursuant to the Federal Power Act: "All rates and charges made, demanded, or received by any public utility for or in connection with the transmission or sale of electric energy subject to the jurisdiction of the Commission . . . shall be just and reasonable and any such rate or charge that is not just and reasonable is hereby declared to be unlawful." 16 U.S.C. Section 824d(a). 78. Defendants are public utilities within the term as used in the foregoing statute. 79. The FERC has specifically found that the prices defendants charged for the wholesale electricity they sold in the California markets during the Class Period were in fact unjust and unreasonable. The California Public Utilities Commission has determined that said unjust and unreasonable prices resulted from the exercise of market power by generators and traders in the California wholesale electricity market. 80. As a result of the conduct described above, the defendants unlawfully acquired money and property from Plaintiff and the Class and have been and will be unjustly enriched at the expense of Plaintiff and the members of the Class. Specifically, defendants have deprived Plaintiff and the Class of billions of dollars in monies from the sale of electricity at unjust, excessive, and manipulated prices. / / / 81. Pursuant to Section 17203 of the California Business & Professions Code, Plaintiff and the members of the class seek an order of this Court restoring to them all money wrongfully taken from them and allowing them a claim in the full amount of all of defendants' ill-gotten gains, so as to restore any and all monies to Plaintiff and the members of the Class and the general public, which were acquired and obtained by means of such unfair and deceptive acts and practices. 82. Pursuant to Section 17203 of the California Business & Professions Code, Plaintiff and the members of the class seek an order of this Court providing appropriate equitable and/or injunctive relief restraining defendants from charging unjust and unreasonable rates and from manipulating the electricity markets. FOURTH CAUSE OF ACTION Violations of the Consumers Legal Remedies Act (Against All Defendants) 83. Plaintiffs reallege and incorporate by reference each and every allegation set forth above. 84. Such acts and practices by defendants violate the Consumer Legal Remedies Act, Civil Code Section 1750, et seq. Defendants' acts warrant appropriate injunctive and declaratory relief, including an injunction restraining them from charging unjust and unfair rates or manipulating the market for electricity. FIFTH CAUSE OF ACTION Unconscionability (Against All Defendants) 85. Plaintiffs reallege and incorporate by reference each and every allegation set forth above. 86. Defendants charged rates for wholesale electricity that were unconscionable given their cost of production, the newly emergent markets for that product, and the consumers' need for electrical power. 87. Defendants' rates were inherently excessive, invalid and unconscionable, particularly in light of the deceptive and manipulative practices they used to inflate the market prices and the vital need consumers and businesses have for electrical power. / / / 88. Defendants' unconscionable prices were passed through to Plaintiff and the majority of the Class in the retail electricity rates charged by SDG&E and thereby directly damaged Plaintiff and the Class. VI. PRAYER FOR RELIEF WHEREFORE, Plaintiff and the Class pray for judgment against defendants as follows: (a) That the Court determine that this action may be maintained as a class action and direct that reasonable notice of this action be given to the members of the Class; (b) That the combination, contract, arrangement, agreement, conspiracy and trust alleged herein be adjudged and decreed to be an unreasonable restraint of trade in violation of Sections 16720 and 16726 of the California Business and Professions Code; (c) That damages be granted according to proof, and that Plaintiff and the Class be awarded treble damages or statutory damages, where applicable, attorneys' fees, costs and disbursements; (d) That Plaintiff and the Class be awarded pre- and post-judgment interest; (e) That the Court enter appropriate injunctive and other equitable relief, including the disgorgement of defendants' unlawful profits; and (f) That Plaintiff and the Class have such other and further relief as the Court may deem just and proper under the circumstances. Dated: November 27, 2000 KRAUSE & KALFAYAN _______________________________ James C. Krause, Esq. Ralph B. Kalfayan, Esq. Patrick N. Keegan, Esq. Stephen W. Poirier, Esq. Attorneys for Plaintiff OF COUNSEL: David B. Zlotnick, Esq., SBN 195607 Attorney at Law 1010 Second Avenue, Suite 1750 San Diego, CA 92101 Tel: (619) 232-0331 Fax: (619) 2 -----Original Message----- From: Mark.Palmer@enron.com [mailto:Mark.Palmer@enron.com] Sent: Wednesday, November 29, 2000 8:44 AM To: Leopold, Jason Subject: Re: FW: ANNOUNCEMENT: Notice of Press Conference today Got the announcement. Can you forward the complaint (suit) as an attachment? Mark ***********
## 103 Richard - in response to your memo dated 7/18/00: First of all I'm confused by her attorney's letter dated 6/27/00 regarding the reference of a per share prices of $8.68 and/or $4.34. I don't have a clue where this number came from. Nowhere in the Securities Purchase Agreement (SPA) is there any reference to a "per share" price. Under Sec. 2.03 all prices referenced are in millions of dollars aggregate format. Pursuant to Sec. 2 of the Securities Purchase Agreement (SPA) dated 7/14/99 ECTMI purchased from the selling shareholders +/- 85% of the stock of Ecogas for an Initial Payment of $23.75 million. Of this amount $3 million was withheld [pursuant to Sec. 2.03(g)] pending Ecogas' successful termination of Montauk's "right of first offer" on or before 12/31/99. This milestone was NOT achieved until March 28, 2000 when a restructuring of various Ecogas/Montauk/GSF (the former jv partnership between Ecogas and Montauk) issues was completed. Therefore, of the $24 million Initial Payment, $3 million was forfeited for Ecogas' inability to meet its obligations pursuant to the SPA. In summary, Article II, Secs. 2.03(b) and (c) of the SPA includes provisions for an Intermediate Payment and Final Payment, respectively, subject to certain milestones being met. These milestones can be described basically as a two-part test. The first criteria requires Ecogas to be producing and selling in excess of 27,000 MMBtu/d of natural gas (methane) and the second criteria being a requirement for Ecogas to be doing son in a profitable manner (i.e., an EBITDA test). Both test criteria are applicable to both the Intermediate and Final payments which means the criteria would have to be satisfied on 4/14/00 and 1/14/01. In theory, both payments and the criteria requirements are part of very complex calculations involving the translation of a number of defined terms within the SPA. Practically speaking, they can be summarized as a) gas production and sales in excess of 27,000 MMBtu/d and b) enough positive/excess cash flow above and beyond operating expenses, G&A and CapEx to provide a minimum rate of return. For the record, on 4/14/00 Ecogas' production and sales rate was approximately 3500-4000 MMBtu/d, significantly below the 27,000 MMBtu/d requirement and their cash flow was actually negative. Theoretically, the criteria could still be met insofar as the Final Payment is concerned since the "deadline" is not until 1/14/01. The obvious issue here is their false misconception that they are due "installment payments." Both the Intermediate and Final Payments referenced above are predicated upon Ecogas achieving two very specific performance-based milestones, neither of which of close to being satisfied as of 4/14/00, the Intermediate Payment date. As I said above, the Final Payment is still outstanding but is likewise subject to Ecogas successfully achieving the same perfomance-based criteria. If they persist and want to have the actual data, I would suggest you have them contact Jerrel Branson (Barbara's former husband) and have him explain the details and/or supply the information. His number in Jackson Hole, WY is (307)734-8664. RANDY ***********
## 118 Got lunch plans? Lets talk about this at 12:30--my joint. (I'll buy) df From: Susan Scott/ENRON@enronXgate on 03/30/2001 03:01 PM To: Glen Hass/ET&S/Enron@ENRON, Mary Kay Miller/ET&S/Enron@ENRON, Maria Pavlou/ENRON@enronXgate, sstojic@gbmdc.com@SMTP@enronXgate cc: Drew Fossum/ET&S/Enron@ENRON, Steven Harris/ET&S/Enron@ENRON Subject: Additional Needles capacity As you'll see in the Legal bullets, TW Commercial has asked me to look into the following issue. As you probably know, SoCalGas is proposing to add compression to its system, which will result in at least 50,000 MMcf/d additional takeaway capacity from Needles by Dec. 2001. TW's shippers have gotten wind of this thru the trade press and are already calling TW to ask about the availability of additional Needles capacity. Many of them are already shippers to Topock who would love to switch their primary point to Needles. The issue is this: One of the reasons Red Rock capacity has been so slow to sell is that we have no more Needles capacity available, and shippers are less enthusiastic about shipping to Topock. Our marketers would like to earmark the new Needles delivery point capacity for use by Red Rock shippers, rather than let existing shippers move primary points. This will help to sell out the expansion space. I am looking thru my file of cases on the topic of reserving capacity for expansions, to see if it gets us anywhere. I'll let you know what I find. If you have comments in the meantime, please let me know. We may need to set up a conference call to discuss this sometime next week. Thanks. ***********
## 122 Note: There will be a briefing meeting on the project in Denver on Nov. 14 N. Border Plans Extension to Tap Powder River Basin Powder River Basin gas producers may be about to get a more direct route to Midwest markets. Northern Border Pipeline has released plans to build a new 325-mile greenfield pipeline from McCabe, MT, to Gillette, WY, to tap the rapidly growing production in the Powder River, which is estimated to hold between 10 and 30 Tcf of gas. The basin's rapid production growth has captured the attention of the industry, with numerous proposed pipelines, including several new interstate plans that would bring gas to the Midcontinent gas grid. Northern Border's project, called the Bison Pipeline, would add a new route out of the basin for between 375,000 and 500,000 MMBtu/d of production starting in November 2003. The pipeline is holding an open season on the proposal through Dec. 5. Receipt points are proposed with Bighorn, Fort Union Gas Gathering, MIGC and Thunder Creek Gas Services. A bi-directional interconnect accessing Williston Basin Interstate Pipeline's Baker storage field also is proposed. The connection with Northern Border will provide Powder River producers with direct access to several local distribution companies, including Montana-Dakota, Alliant Interstate Power, MidAmerican Energy, Nicor Gas, People Gas and Nipsco. It also provides access to ANR Pipeline, Midwestern, NGPL, Northern and Vector. The projected recourse rate is 26 cents/Dth/d, but a negotiated 10-year demand rate cuts 4 cents off that. The pipeline is offering "mileage-sensitive rates," which are expected to allow a shipper to tailor supply access and optimize transportation value. Northern Border plans to hold a briefing on the project in Denver on Nov. 14. For more information, contact project director Paul Miller in Omaha, NB, at (402) 398-7758 or James Hoff in Denver at (303) 575-6475, or see the pipeline's Web site at www.northernborderpartners.com. ***********
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## tariff taylorhouectect team technolog tell term termin texa thank that
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## therefor thing think third though thought three thursday tim time
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## 103 1 0 0 0 0 0 0 0 0 0
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## 122 0 0 0 0 0 0 0 0 0 0
## today togeth told tom tomorrow top total trade trader transact
## 1 0 1 0 0 0 0 0 4 0 1
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## 98 3 0 1 0 0 1 3 26 16 4
## 103 0 0 0 0 0 0 0 0 0 0
## 118 0 0 0 0 0 0 0 1 0 0
## 122 0 0 0 0 0 0 0 0 0 0
## transfer transmiss transport tri tuesday turn two type understand unit
## 1 0 0 0 0 0 0 0 0 0 3
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## 98 0 10 6 0 0 0 4 0 0 3
## 103 0 0 0 0 0 0 1 0 0 0
## 118 0 0 0 0 0 0 0 0 0 0
## 122 0 0 1 0 0 0 0 0 0 0
## unless updat upon use util valu various version via view vinc visit
## 1 0 0 0 1 0 0 0 0 0 1 0 0
## 2 0 0 0 1 0 0 0 1 0 0 0 1
## 31 0 0 0 0 1 0 0 0 0 0 0 0
## 46 0 0 0 0 0 0 0 0 0 3 0 0
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## 82 0 0 0 0 0 0 0 0 0 0 0 0
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## 103 0 0 1 0 0 0 1 0 0 0 0 0
## 118 0 0 0 1 0 0 0 0 0 0 0 0
## 122 0 0 0 0 0 1 0 0 0 0 0 0
## volum want washington water way web websit wednesday week well west
## 1 0 2 0 0 0 0 0 0 0 2 0
## 2 0 0 0 0 0 0 0 0 0 1 0
## 31 0 0 0 0 0 0 0 0 0 0 0
## 46 0 1 0 0 2 0 0 0 0 1 0
## 79 0 0 0 0 0 0 0 0 0 2 0
## 82 0 0 0 0 0 0 0 0 0 0 0
## 98 0 0 0 0 1 1 0 1 0 1 0
## 103 0 1 0 0 0 0 0 0 0 0 0
## 118 0 0 0 0 0 0 0 0 1 0 0
## 122 0 0 0 0 0 1 0 0 0 0 0
## whether wholesal will william within without word work year yesterday
## 1 0 0 6 0 0 0 0 8 0 0
## 2 0 0 2 0 0 0 0 0 0 0
## 31 0 0 1 0 1 0 0 0 0 0
## 46 1 0 1 0 0 0 0 3 0 0
## 79 1 0 5 0 0 1 0 6 1 0
## 82 0 0 1 0 0 0 0 0 0 0
## 98 6 50 13 4 4 0 0 2 5 0
## 103 0 0 0 0 1 0 0 0 0 0
## 118 0 0 2 0 0 0 0 0 0 0
## 122 0 0 2 0 0 0 0 0 0 0
## yet york responsive.fctr.predict.Final.rpart.prob
## 1 0 0 0.1042048
## 2 0 0 0.1042048
## 31 0 0 0.1042048
## 46 1 0 0.1042048
## 79 1 0 0.1042048
## 82 0 0 0.1042048
## 98 0 0 0.7843137
## 103 0 0 0.1042048
## 118 0 0 0.1042048
## 122 0 0 0.1042048
## responsive.fctr.predict.Final.rpart
## 1 N
## 2 N
## 31 N
## 46 N
## 79 N
## 82 N
## 98 Y
## 103 N
## 118 N
## 122 N
## responsive.fctr.predict.Final.rpart.accurate .label
## 1 TRUE .1
## 2 TRUE .2
## 31 FALSE .31
## 46 FALSE .46
## 79 FALSE .79
## 82 FALSE .82
## 98 TRUE .98
## 103 FALSE .103
## 118 FALSE .118
## 122 FALSE .122
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
glb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="predict.data.new",
chunk_step_major=max(glb_script_df$chunk_step_major)+1,
chunk_step_minor=0,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))## chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed11 fit.data.training.all 6 1 994.875
## elapsed12 predict.data.new 7 0 1001.017Step 7: predict data.new
# Compute final model predictions
glb_newent_df <- glb_get_predictions(glb_newent_df)
glb_analytics_diag_plots(obs_df=glb_newent_df)
## email
## 7 As you know, the ENA gas floor came in with a $30 million VAR position today. Even though this left us with another $10 million of unused VAR, I want to make you aware that we may exceed our VAR limit today. This move up was much bigger than the market anticipated, and natural gas volatility was up quite a bit. The VAR calculation is now kicked off at 2:00am and we will know our VAR position by 6:00am. If we are over, I will need to ask for a one day increase. I have already told the desk heads that we will most likely need to sell some contracts tonight and tomorrow. Jeff ***********
## 9 Jason, The May 30th cut to Peoples ANR contract 104684 is now 4,856 MMBtu. ANR revised its morning allocation. It now confirms 35,144 for Peoples Gas for May 30th. The previous revised delivered amount of 33,401 has been adjusted upward by 1,743 to 35,144. Peoples will reduce its SIQ purchase by 4,856. Therefore, 125,000 of SIQ becomes 120,144 for May 30th. Jer > ---------- > From: \tSlechta, Jerry > Sent: \tThursday, May 31, 2001 10:44 AM > To: \t'Jason Williams' > Cc: \t'Barbara Dillard Radous'; 'Cora Pendergrass'; 'Mark Mixon'; Holler, > Sonia R.; Wear, Dave; Akkerman, Antje; Thomas, Lilian; Hayes, Robert C. > Subject: \tCut on ANR to Peoples Gas Contract 104684 > > Jason, > \tI verbally confirmed the cut that occurred for gas day May 30th with > Irene Isais of our Gas Transportation Services department. Peoples ANR > contract 104684 was cut by 6,599 MMBtu throughout the May 30th gas day. > > The supply was cut because PGL Gas Supply was not told by Enron North > America that the Northern Border supply @ Will County had been reallocated > on different contracts. All four GISB cycles were exercised in an attempt > to receive the entire 40,000 but since the contract mismatch was not > corrected until the final cycle, Peoples lost 6,599 MMBtu for the day. > > Peoples reduced its SIQ purchase from 125,000 to 118,401 for gas day May > 30th. > > \t\t\t\t\t\tJer > ---------------------------------------------------------------- The information transmitted is intended only for the person or entity to which it is addressed and may contain confidential and/or privileged material. Any review, retransmission, dissemination or other use of, or taking of any action in reliance upon, this information by persons or entities other than the intended recipient is prohibited. If you received this in error, please contact the sender and delete the material from any computer. ***********
## 27 Are we still on for lunch Wed.? (1) The PSEG tolling agreement is super high priority right now, b/c of mkt volatility, so I'm marking up the Black Hills deal (lots of it is not relevant or helpful b/c there are dispatch blocks in the PSEG deal, which need to be addressed somehow) and will speak to you tomorrow, I hope , after meeting w/ the commercial guys again. (2) New deal from Woody -- Wisvest is trying to unload the SOS load they have for Connecticut Light & Power -- wholesale contract, about 11% of the CT SOS load and Dana is interested in doing this one. Wisvest wants an MOU by next week. Just got the CA this evening and am working on it so we can get the info needed for evaluation. (3) TEPAC is still pretty hot, b/c TVA's short power, so this would be a good time to have a deal w/ them. I still need to meet w/ everybody and go over the remaining risks on that one (4) Reliant - Dave Fairley wants to do a 50 MW deal w/ them in FL just like the one we did w/ Reedy Creek, w/ a few wrinkles I haven't fully learned about yet, except that they own 2 units and about 50 MW from another unit; he's not sure who the interconnects are with; Reliant has no native load, of course, so the FM issues will be different. Another one that has to close very soon b/c the price is only $26. (5) E Trans is at it again, but all they need at the moment is a couple more confidentiality agreements so they can start talking to some new players. Apparently, the National Grid is interested in operating an RTO and PJM is forming its own jv to provide services to RTOs and they all want us to have some kind of role -- getting interesting again. Will keep you posted. Regards, Janice EB3861 Assistant General Counsel, Enron North America Corp. 713-853-1794 (Fax: 713-646-4842) ***********
## 47 Eric & John ECT Merchant Investments Corp. has entered into a Volumetric Production Payment agreement with St. Mary's Production LLC which provides for the dedication of St. Mary's non-operated gas production from the Hunt Oil Company operated Myette Point Northwest Field. ECT Merchant Investments Corp. has arranged for the Enron North America Upstream Wellhead Desk to manage the sale, nomination and transport of this production. Could you please get us a quote to purchase this wellhead gas on a firm basis based on the following: a. 18 month term commencing 1/1/2001 and ending 6/30/2003 b. firm purchase and firm transport for 90% of the wellhead volume, starting at 90% of 11,567 per day and declining as defined on the last tab labelled "Volume" on the spreadsheet attached c. gas daily purchase and interruptible transport for the remaining 10% of the wellhead volume Enron Upstream will keep the desk financially whole should volumes for the 90% firm component fail to show up on any given day of the 18 month term Drafts of proposed Deal Tickets are attached. George x3-6992 ***********
## 110 I just left Derrick a voicemail so I should have an answer shortly. "Fergus, Gary S." <GFergus@brobeck.com> 02/02/2001 08:50 AM To: "Richard B. Sanders Esq. (E-mail)" <richard.b.sanders@enron.com> cc: Subject: Request for Conflict Waiver to Represent City of Palo Alto in Ban kruptcy matters Richard, Here is the status of the information we have about the City of Palo Alto's request that our firm represent them in connection with the potential bankruptcy of PG&E. It is my understanding that the City of Palo Alto has agreed that in the event that Enron and the City of Palo Alto become adverse in any proceeding, then agree that our firm will have to withdraw from representation of the City of Palo Alto and that we will be able to continue to represent Enron notwithstanding the fact that they will have provided to our firm confidential information. In particular, if Enron and the City of Palo Alto become adverse in connection with the California electricity litigation (including class actions, governmental investigations and the recent decision of Enron Energy Services to transfer customers back to PG&E for physical delivery), our firm will be able to continue to represent Enron even against the City of Palo Alto. One of the reasons that the City of Palo Alto is willing to do this is that they do not believe that it is likely they will become adverse to Enron and their desire to have the specialized expertise of Larry Engel. The City of Palo Alto has its own generation capacity through WAPA (the Western Area Power Administration), NCPA (the Northern California Power Agency), and TANC (the Transmission Authority of Northern California). The City does buy a small amount of power from Enron, as I understand it. We have specifically identified the City and County of San Francisco suit and asked if Palo Alto has any intention of joining that suit. Would you please let us know Enron's view of this proposed bankruptcy representation in view of these disclosures? Thanks Gary ======================================================= This email message is for the sole use of the intended recipient(s) and may contain confidential and privileged information. Any unauthorized review, use, disclosure or distribution is prohibited. If you are not the intended recipient, please contact the sender by reply email and destroy all copies of the original message. To reply to our email administrator directly, send an email to postmaster@brobeck.com BROBECK PHLEGER & HARRISON LLP http://www.brobeck.com ***********
## 111 ----- Forwarded by Richard B Sanders/HOU/ECT on 11/20/2000 07:51 AM ----- "Meringolo, Peter" <PMeringolo@brobeck.com> 11/16/2000 03:27 PM To: "'Richard.B.Sanders@enron.com'" <Richard.B.Sanders@enron.com>, "'msmith1@enron.com'" <msmith1@enron.com>, "'Mary.Hain@enron.com'" <Mary.Hain@enron.com>, "'David_Aamodt@pgn.com'" <David_Aamodt@pgn.com>, "'Christian.Yoder@enron.com'" <Christian.Yoder@enron.com>, "'mday@gmssr.com'" <mday@gmssr.com>, "Fergus, Gary S." <GFergus@brobeck.com>, "Stephen C. Hall (E-mail)" <schall@stoel.com> cc: Subject: Draft letter to the CPUC Please let either Gary or me know your thoughts and comments regarding the below draft letter to the CPUC. Thanks, Peter > PRIVILEGED AND CONFIDENTIAL > ATTORNEY CLIENT COMMUNICATION > ATTORNEY WORK PRODUCT > > > > Harvey Morris Esq. > California Public Utilities Commission > 505 Public Utilities Commission > San Francisco, California 94102 > > Re: I.00-08-002 Subpoenas Served on Enron Power Marketing, Inc. > ("EPMI"), Enron Energy Services Operations Inc. and Enron Energy Services > Inc. (collectively referred to as "EES"), Enron Energy Marketing > Corporation ("EEMC"), and Portland General Electric Corporation ("Portland > General")(collectively sometimes referred to as the "Enron Entities") > > Harvey, > > I am writing in response to the various voicemail exchanges and > phone calls between Michael Day, on behalf of the Enron Entities, and > yourself during the past few weeks. Here is the current status as we > understand it. Based upon the agreements we reached between October 6, > 2000 and October 18, 2000 (date of your confirming email) the Enron > Entities have made the following document productions in response to the > subpoenas served by California Public Utilities Commission (the > "Commission"): > > * October 13, 2000 Bate Nos. Enron Entities 000001-000025 on behalf > of all Enron Entities Responsive to Request Nos. 1-11, 20. > > * October 27, 2000 Bate Nos. P000001-002115 on behalf of Portland > General Responsive to Request Nos. 12, 14, 16 and 18. > > * November 14, 2000 Bate No. Enron Entities 000026 (Diskette with > EES/EEMC retail data) Responsive to Request Nos. 7 to 11, 13, 15 to 17, > and 19. > > * November 15, 2000 Bate No. P002116-002117 (Diskettes with Portland > General Data, which include approximate 355,000 wholesale transactions) > Responsive to Request Nos. 7 to 11, 13 to 17, and 19. > > We believe with these productions, we have complied with the October > 6-18th agreement with respect to Portland General and EES/EEMC. As you > know, these productions were all made reserving the Enron Entities rights > to object or challenge the subpoenas and reserving any claims, defenses, > objections, jurisdictional or otherwise, or other responses. > > We originally had hoped to have all of the Transactional Data for > EPMI, as defined in our October 6th email, available for production by > October 27th. However, we were also mindful of your expressed expectation > that any data provided be useful and reliable. The collection of data you > requested in the subpoenas is neither kept electronically in the same > location nor organized as you requested it. In fact during our review of > some of the data we found significant errors that called into question the > methodology by which the data was extracted from existing information. > When it became apparent that we could not accurately estimate how long it > would take to get reliable data given the volume of transactions involved, > we informed you of the problem. We now understand that if we cannot give > you a firm date when the balance of the Transactional Data will be > available, the Commission believes it will have no choice but to seek to > compel production before the Federal Regulatory Energy Commission > ("FERC"). We understand the pressure that you must be under to get data > immediately, but we cannot, at this time, give you another date in the > immediate future by which we are certain that we will be able to produce > accurate data. > > Moreover, during the iterim between October 18th and today, a number > of significant events have occurred that we believe bear on the subpoenas > served on the Enron entities. First, we understand that many other > parties who were served with the same subpoenas have not provided the > detailed information requested and have objected on a variety of very > sound legal grounds. Second, the Commission itself has, without a hearing > on the motion, unilaterally revised the proposed protective order that is > supposed to govern these productions and summarily denied other changes > sought by other parties served. Third, the Commission has filed its > motion before FERC seeking an order compelling the production of the data > from those other parties that have objected and seeking an expedited > hearing and production schedule. We note that the reasons stated in the > motion before FERC as the basis for the production are materially > different than the reasons originally stated in the ex parte application > to the Commission's own Administrative Law Judge who issued the subpoenas. > Finally, FERC itself has completed its investigation and has issued a > tentative order as of November 1, 2000. > > Originally, the Enron Entities had been prepared to accept "most > favored nation" treatment with respect to protective orders and requests > by others to limit the scope of the subpoenas and produce information that > was available. However, given both the Commission's stated urgent need > for an immediate answer that EPMI cannot in good faith give despite > substantial effort (date for production of reliable and accurate > Transaction Data) and the uncertainty created by the above described > intervening events, EPMI believes it has no choice but to serve its formal > objections and responses to the subpoenas and await the Commission's > threatened motion to compel before FERC. > > We also understand from our discussions with you that the Commission > has created various lists or categories for the entities that have been > served with subpoenas. We understand that there is a list for those > entities that have been cooperating, a list for those who have been > delaying and a list for those who have been objecting and exercising their > rights to challenge the subpoenas. We understand that the Commission > intends to punish, by whatever means it has available, those entities that > object and exercise their rights to challenge the subpoenas. We deplore > these tactics, but despite all of the efforts to cooperate described > above, we understand that EPMI may in fact be moved from the cooperating > list to the group to be punished for exercising its rights. > > If you have and questions or comments, please do not hesitate to > contact me. > > Sincerely, > > > > Gary S. > Fergus ======================================================= This email message is for the sole use of the intended recipient(s) and may contain confidential and privileged information. Any unauthorized review, use, disclosure or distribution is prohibited. If you are not the intended recipient, please contact the sender by reply email and destroy all copies of the original message. To reply to our email administrator directly, send an email to postmaster@brobeck.com BROBECK PHLEGER & HARRISON LLP http://www.brobeck.com ***********
## 128 An overview of NERC's Reliability Assessment 2000-2009 ,Copyright 2000 EEI. All rights reserved. EEI's State Restructuring Service is proud to present an Internet E-Forum providing an overview of the North American Electric Reliability Council's (NERC) recently released Reliability Assessment 2000-2009, the reliability of bulk electric systems in North America. The reliability of the electric generation and transmission systems serving North America has come under increased federal government, state regulator, and customer scrutiny. Generation capacity margins have decreased and the bulk electric systems are subjected to flows in magnitudes and directions not contemplated when they were designed or for which there is minimal operating experience. Each year, the Reliability Assessment Subcommittee (RAS) of the North American Electric Reliability Council (NERC) reviews the overall reliability of existing and planned electric generation and transmission systems in North America. This year's Reliability Assessment 2000-2009 report presents: *An assessment of electric generation and transmission reliability through 2009 *An assessment of the generation resource adequacy of each Interconnection in North America *A discussion of key issues affecting reliability of future electric supply *Regional assessments of electric supply reliability, including issues of specific Regional concern Presenting an overview of this year's Reliability Assessment is Frank J. Koza, Jr., Chairman, Reliability Assessment Subcommittee, and General Manager, Ventures, PECO Energy Company. Our guest moderator is Tim Gallagher, Mgr Technical Services, NERC To provide questions and commentary we have Jolly Hayden, Vice President of Transmission, Dynegy A copy of the Reliability Assessment 2000-2009 is available from the NERC web site at http://www.nerc.com/~pc/syscond.html. November 21, 2000 1 p.m. ET Note: Please block a 2-hour time slot for the E-Forum. We expect the program to take about one hour. Q&A at the conclusion of the E-Forum could take up to an additional hour. Registration Cut-off and Cancellation Policy: All registrations must be received by November 17, 2000. Substitutions may be made at any time, however, no refunds will be made for cancellations received after November 17, 2000. Registration Cost: EEI/NARUC/NCSL Members: $50.00 / Associates $75 / Non-members: $100.00 ------------------------------------------------------------------------------ -------------- FOR MORE INFORMATION AND/OR TO REGISTER, ACCESS THE FOLLOWING URL: www.eei.org/resources/meetings/forums/ ------------------------------------------------------------------------------ -------------- E-Forums are real-time, interactive presentations, created, produced and distributed by EEI. The visual presentation is delivered over the Internet, while the audio portion is delivered through a conference phone call. You can participate in an E-Forum using any computer with Internet access and a telephone. To fully participate in the E-Forum, you will need: - Access to the Internet and a web browser. - A separate voice phone line and a phone to dial into the audio conference call. Cell phones can be used. - Upon confirmation of your registration, you will be given complete instructions including the URL (with Password/ID) and a the conference phone number. - Slides used in the presentation will also be e-mailed to you prior to the forum and will soon be available over the Internet. For more information on EEI's E-Forums, contact Mike Oldak, Director, State Competitive and Regulatory Policy at 202-508-5523 moldak@eei.org or Leigh Miller, Coordinator at (202) 508-5522 lmiller@eei.org For more information on EEI's State Restructuring Service, contact Norm Jenks, Manager, Regulation and Competitive Analysis 202-508-5533 njenks@eei.org ,Copyright 2000 by the Edison Electric Institute All rights reserved under U.S. and foreign law, treaties and conventions. No part of this work may be distributed, redistributed, reproduced or copied in any form or by any means - graphic, electronic or mechanical, including photocopying, recording, taping, or information storage and retrieval systems without written permission of the publisher. ***********
## 132 ---------------------- Forwarded by Michael Moran/GPGFIN/Enron on 12/11/2000 04:31 PM --------------------------- Mary Hain@ECT 12/11/2000 04:12 PM To: Christopher F Calger/PDX/ECT@ECT, Tim Belden/HOU/ECT@ECT, Greg Wolfe@ECT, Mike Swerzbin/HOU/ECT@ECT, Matt Motley/PDX/ECT@ECT, Tom Alonso/PDX/ECT@ECT, Mark Fischer/PDX/ECT@ECT, Robert Badeer/HOU/ECT@ECT, Shelley Corman/ET&S/Enron@ENRON, Phillip K Allen/HOU/ECT@ECT, Michael Moran/ET&S/Enron@ENRON, Dave Parquet, Jake Thomas, Jeff Richter/HOU/ECT@ECT, Sean Crandall/PDX/ECT@ECT@ECT, Diana Scholtes/HOU/ECT@ECT, John M Forney/HOU/ECT@ECT, Richard Sanders cc: Subject: SDG&E Asks FERC to cap capacity release rates bchen@newenergy.com (Bill Chen) on 12/08/2000 12:13:42 PM To: arm@phaser.com, jleslie@luce.com cc: Subject: Dec. 7 - SDG&E Press Release CRECers, Note attached SDG&E press release calling on Governor Davis to use his emergency powers to remedy the accelerating energy crisis by taking a number of actions, e.g., directing local air districts to temporarily lift emissions limits for in-state power plants so that the plants which have met or exceeded their air-emissions limits can run. In addition, SDG&E reported it made an emergency filing with FERC requesting an immediate reinstatement of price caps on interstate natural gas pipeline transportation to the CA border. Bill - Sempra PR.doc ***********
## 227 Janie: Here is a summary of the new structure that we discussed for Harbor. 1. Each day we would call in our estimate of usage for the next day. On friday we would call in our estimate for Sat - Mon. 2. The desk would bill us ON USAGE, not on deliveries or ordered. 3. EPMI would pay the mid point of Gas Daily. 4. EPMI would pay a demand charge of $.025/MMBtu based on a max DQ of 20,000 MMBtu/d times the number Let me know if this works for you. If so, we will try to close the deal with the customer. If you could call me back today that would be great. C ***********
## 592 Jeff--- Which paper published the article below? ---Jennifer From: Jeff Dasovich@ENRON on 04/26/2001 05:42 PM Sent by: Jeff Dasovich@ENRON To: Alan Comnes/PDX/ECT@ECT, Angela Schwarz/HOU/EES@EES, Beverly Aden/HOU/EES@EES, Bill Votaw/HOU/EES@EES, Brenda Barreda/HOU/EES@EES, Carol Moffett/HOU/EES@EES, Cathy Corbin/HOU/EES@EES, Chris H Foster/HOU/ECT@ECT, Christina Liscano/HOU/EES@EES, Craig H Sutter/HOU/EES@EES, Dan Leff/HOU/EES@EES, Debora Whitehead/HOU/EES@EES, Dennis Benevides/HOU/EES@EES, Don Black/HOU/EES@EES, Dorothy Youngblood/HOU/ECT@ECT, Douglas Huth/HOU/EES@EES, Edward Sacks/Corp/Enron@ENRON, Eric Melvin/HOU/EES@EES, Erika Dupre/HOU/EES@EES, Evan Hughes/HOU/EES@EES, Fran Deltoro/HOU/EES@EES, Gayle W Muench/HOU/EES@EES, Ginger Dernehl/NA/Enron@ENRON, Gordon Savage/HOU/EES@EES, Harold G Buchanan/HOU/EES@EES, Harry Kingerski/NA/Enron@ENRON, Iris Waser/HOU/EES@EES, James D Steffes/NA/Enron@ENRON, James W Lewis/HOU/EES@EES, James Wright/Western Region/The Bentley Company@Exchange, Jeff Messina/HOU/EES@EES, Jeremy Blachman/HOU/EES@EES, Jess Hewitt/HOU/EES@EES, Joe Hartsoe/Corp/Enron@ENRON, Karen Denne/Corp/Enron@ENRON, Kathy Bass/HOU/EES@EES, Kathy Dodgen/HOU/EES@EES, Ken Gustafson/HOU/EES@EES, Kevin Hughes/HOU/EES@EES, Leasa Lopez/HOU/EES@EES, Leticia Botello/HOU/EES@EES, Mark S Muller/HOU/EES@EES, Marsha Suggs/HOU/EES@EES, Marty Sunde/HOU/EES@EES, Meredith M Eggleston/HOU/EES@EES, Michael Etringer/HOU/ECT@ECT, Michael Mann/HOU/EES@EES, Michelle D Cisneros/HOU/ECT@ECT, mpalmer@enron.com, Neil Bresnan/HOU/EES@EES, Neil Hong/HOU/EES@EES, Paul Kaufman/PDX/ECT@ECT, Paula Warren/HOU/EES@EES, Richard L Zdunkewicz/HOU/EES@EES, Richard Leibert/HOU/EES@EES, Richard Shapiro/NA/Enron@ENRON, Rita Hennessy/NA/Enron@ENRON, Roger Yang/SFO/EES@EES, Rosalinda Tijerina/HOU/EES@EES, Sandra McCubbin/NA/Enron@ENRON, Sarah Novosel/Corp/Enron@ENRON, Scott Gahn/HOU/EES@EES, Scott Stoness/HOU/EES@EES, Sharon Dick/HOU/EES@EES, skean@enron.com, Tanya Leslie/HOU/EES@EES, Tasha Lair/HOU/EES@EES, Ted Murphy/HOU/ECT@ECT, Terri Greenlee/NA/Enron@ENRON, Tim Belden/HOU/ECT@ECT, Tony Spruiell/HOU/EES@EES, Vicki Sharp/HOU/EES@EES, Vladimir Gorny/HOU/ECT@ECT, Wanda Curry/HOU/EES@EES, William S Bradford/HOU/ECT@ECT, Kathryn Corbally/Corp/Enron@ENRON, Jubran Whalan/HOU/EES@EES, triley@enron.com, Richard B Sanders/HOU/ECT@ECT, Robert C Williams/ENRON_DEVELOPMENT@ENRON_DEVELOPMENT, Greg Wolfe/HOU/ECT@ECT, James Wright/Western Region/The Bentley Company@Exchange, Dirk vanUlden/Western Region/The Bentley Company@Exchange, Steve Walker/SFO/EES@EES, Jennifer Rudolph/HOU/EES@EES, Martin Wenzel/SFO/HOU/EES@EES, Douglas Condon/SFO/EES@EES, wgang@enron.com, Scott Govenar <sgovenar@govadv.com>, Hedy Govenar <hgovenar@govadv.com> @ ENRON, jklauber@llgm.com, Mike D Smith/HOU/EES@EES, John Neslage/ENRON_DEVELOPMENT@ENRON_DEVELOPMENT, Janel Guerrero/Corp/Enron@Enron, Eric Letke/DUB/EES@EES, Richard B Sanders/HOU/ECT@ECT, gfergus@brobeck.com, Michael Tribolet/ENRON@enronXgate, Robert Frank/NA/Enron@Enron, Richard B Sanders/HOU/ECT@ECT, gfergus@brobeck.com, Susan J Mara/NA/Enron@ENRON cc: Subject: CA Legislative Committee Hearing on California Border Gas Prices FYI. This is the presentation that the Brattle Group---a consulting group that Edison has long used on gas issues in California---gave last week to the legislative committee investigating wholesale gas prices at the California Border. The day following this presentation (which was followed by a presentation by the California PUC's FERC lawyer), the committee called El Paso and Dynegy to respond to Brattle's and the CPUC FERC lawyer's presentations. Also attached is the best synopsis of the hearing that I've seen in the press. Best, Jeff ****************************************************************************** **************************************************** Competing evidence clouds Calif. investigation Prompted in part by the California Public Utilities Commission, the California Assembly has been scrutinizing the role that interstate pipelines have played in the state\001,s current energy crisis. In the efforts to find a smoking gun, legislators have leaned heavily on a report prepared by The Brattle Group, a consultancy commissioned by utility Southern California Edison to dig up evidence of market power abuse. But the state\001,s biggest transporter of gas to California -- El Paso Natural Gas -- is not ready to take the rap. The pipeline has commissioned its own study, which it recently presented as evidence that it has not circumvented any laws or regulation. As reported in both the trade press and national media, SoCal Ed and the CPUC are pointing the finger of blame at El Paso for alleged manipulation of California border prices through affiliate deals and capacity hoarding. And exhibit A in their case against El Paso is The Brattle Group\001,s study of the California market. Richard Zeiger, a spokesman for Assembly Member Darrell Steinberg, chairman of the California Assembly Judiciary Committee, told Gas Daily that The Brattle Group\001,s market study proved that the surge in gas prices at the California border was not caused by normal market forces (GD 4/20). His remarks followed an oversight hearing during which Assembly members questioned Dynegy and El Paso officials about their involvement in the California market. El Paso presented a different version of events to the Assembly. In a report presented to legislators, a research group hired by El Paso concluded that a convergence of factors, not a conspiracy, caused the price run-up. Lukens Consulting Group, a Houston-based consultancy, was retained by El Paso to conduct work on several fronts. In its study of the California market, Lukens concluded that the increasing convergence of the gas and electricity businesses was one of the main culprits in the California gas price imbroglio. Assemblyman John Campbell, a Republican member of the oversight committee, said he "didn\001,t see any smoking gun" in either report. "We had our committee hearing, and we certainly had a lot on the Brattle Study and a little on the Lukens study. To some degree, I\001,m not sure that the California legislature is the best place to adjudicate the differences between these two studies," Campbell said. "I believe FERC is looking at this situation " and it would seem to me that that\001,s the appropriate place." Campbell said that the CPUC had been prodding the California legislature to give support to its claims of market power abuse by pipelines. "It\001,s being pushed basically by the Public Utilities Commission here, which believes that there was collusion" by pipeline companies to push up gas prices in California, he said. The CPUC, Campbell suggested, sought satisfaction before the California assembly when it had failed on the federal level: "There\001,s a concerted effort, not just on natural gas but on other things here in California, for entities and organizations here to point the finger elsewhere for the problems that we\001,re having in this state and I think you\001,re seeing some of that with the public utilities commission." Whether either report wins over the public incensed by high natural gas prices is a different matter entirely. In the meanwhile, the dueling California market studies seem to have taken on a life of their own. The Brattle Group Study, for instance, has become the center of a heavily litigated effort to force FERC to compel the release of market data by California market participants. Following on a request by SoCal Ed, which said it needed additional data to round out The Brattle Group report, FERC Chief ALJ Curtis Wagner issued subpoenas to the other three major pipelines that serve the state as well as to Sempra Energy Trading. Several parties resisted FERC\001,s call for market information, saying the requested data contained commercially sensitive information. FERC allowed the discovery process to move forward but only after attaching strict data protection rules restricting access to evidence (GD 4/23). Critics of the pipeline industry have already suffered one setback in their case. The commission recently dismissed the CPUC\001,s claim that El Paso rigged the auction of a large block of pipeline capacity in favor of affiliate El Paso Merchant Energy. In addressing the California Assembly, representatives of Dynegy said that FERC\001,s recent ruling on the California border controversy obviated the need for more investigation. The controversy, however, is far from over. FERC last month also ordered a hearing into whether El Paso Natural Gas and its affiliates manipulated capacity to drive up the price of gas delivered into California (GD 3/29). That hearing is likely to take place this summer. (RP00- 241, et al.) NH ----- Forwarded by Jeff Dasovich/NA/Enron on 04/26/2001 05:23 PM ----- Douglas.Porter@sce.com 04/19/2001 11:36 AM To: jeff.dasovich@enron.com cc: Subject: Sacramento Pres Final 4_13_01(projected).ppt Per your request, attached are the presentation slides from yesterday. Douglas Porter, Senior Attorney Southern California Edison Company (626)302-3964 (626)302-3990(fax) douglas.porter@sce.com(See attached file: DRT2486.PPT) - DRT2486.PPT ***********
## 796 Class Action Lawsuits Alleging Restraint of Trade and Unfair Business Practices Filed Against SoCal Gas, San Diego Gas & Electric, And El Paso Natural Gas LOS ANGELES, Dec. 18 /PRNewswire/ via NewsEdge Corporation - Two related class action lawsuits have been filed in the California Superior Court in Los Angeles, against Southern California Gas Company, San Diego Gas & Electric Company, Sempra Energy (parent of the two utilities), El Paso Natural Gas Co., and other companies related to El Paso Natural Gas. The two lawsuits allege a conspiracy resulting in restraint of trade and unfair competition and unlawful business practices. The complaints summarize the issue as follows: This action involves a massive conspiracy to eliminate competition in the newly deregulated energy industry that has resulted in endangering California's electrical system and threatening California's economy. It is perhaps the largest gouging of energy consumers in American history. Southern California's current "energy crisis" is not simply the result of ever-increasing demand by a growing population for energy. Rather, it is the direct result of a conspiracy among the natural gas industry's most powerful Southern California players to preserve and maintain the market dominance that they enjoyed for many years as monopolies subject to regulation. When the artificial monopoly created by regulation was disassembled, those dominant companies took prompt and illegal action to ensure that they would not lose the benefits of their market power. Their unlawful collusion has contributed significantly to the recent astronomical increases in the price of natural gas and electricity. As a result, Southern California customers have had to pay billions of dollars extra for their natural gas and electric needs. This lawsuit seeks to recover those damages. (Para. 1 and 2 of complaint.) "In September of 1996, top executives of Southern California Gas Company ("SoCal Gas"), San Diego Gas & Electric ("SDG&E") and El Paso Natural Gas Corporation ("EPNG") met at the Embassy Suites Hotel, near Sky Harbor Airport in Phoenix, Arizona. Fearing a new era of open competition and lower prices, these latter day captains of industry gathered secretly to hatch a conspiracy to dominate the unregulated aspects of the natural gas and electricity markets. At the meeting, these three companies, who together dominate the Southern California natural gas market, illegally agreed not to compete against each other in the Southern California and Baja California natural gas delivery markets. They also conspired to prevent other pipelines from being built that would have competed against them and lowered natural gas prices in these markets. The conspirators sought to eliminate competition, take advantage of electric deregulation, drive up the price of natural gas, and profit from the increased prices." (Para 4 of complaint.) One of the two lawsuits addresses the effects of the conspiracy on natural gas prices; the other deals with the effects on electricity prices. (The price of gas-fired generation establishes the price for most of the electricity used in Southern California.) Copies of the two lawsuits filed this morning are available as Adobe Acrobat (PDF) files on the web site of the law firm of O'Donnell and Shaeffer at http://oslaw.com/osnews. Attorneys representing the plaintiffs are available for interviews, and can also provide additional materials, including copies of the agenda prepared for the secret meeting, as well as graphics illustrating the location of natural gas pipelines that were planned, but then terminated by the defendants following the secret meeting in Phoenix. For more information contact: Pierce O'Donnell or Carole E. Handler, O'Donnell & Shaeffer LLP, (213) 532-2000 SOURCE O'Donnell & Shaeffer LLP CONTACT: Pierce O'Donnell or Carole E. Handler of O'Donnell & Shaeffer LLP, 213-532-2000 Web site: http://oslaw.com/osnews E SOURCE Study: Customer-Side Electricity Demand Reduction Could Stem Rising Power Prices BOULDER, Colo., Dec. 18 /PRNewswire/ via NewsEdge Corporation - With electricity prices rising like floodwaters in California and other states, limited generation capacity has garnered the most attention as the culprit. Many see constructing new power plants as the sole solution for bringing prices down. However, E SOURCE research just out of the field indicates that convincing customers to reduce their electricity demand in exchange for cash could provide tremendous price relief in open power markets. "Our study shows that commercial and industrial customers are very willing to reduce demand if the price is right, if mechanisms are in place for communicating those prices, and if they have a means of controlling their electric loads. We found that larger customers could offer almost 20 percent of their load as negawatts -- temporary reductions in electricity demand," says E SOURCE Vice President Bill LeBlanc. Because most electricity use is considered essential and rising wholesale prices don't immediately affect end users, prices can go sky high when power supplies get tight. But if demand dropped in response to high prices, LeBlanc contends that overall power costs would be much lower. He explains, "Although there's a disconnect between price and demand in the marketplace today, we've uncovered a significant subset of customers who seem ready to participate in a dynamic pricing market. Unfortunately, most of them don't yet have the communication and control tools that would make load management possible. They also haven't been offered a pricing product they like." The E SOURCE research study confirms that customers want flexibility in deciding whether to participate in load reductions on a daily or even hourly basis, as just one example. So how many negawatts are available? According to the E SOURCE national market survey, medium-to-large commercial and industrial customers could potentially reduce their demand by 18 percent if offered a market price of $1 per kWh (one of the tested price points) for reduced electricity usage. At this price -- which, incidentally, is what Edison International CEO and President John Bryson reported that his company paid for electricity on December 12 in wholesale power markets -- about 55,000 MW could be made available across the U.S., and about 4,800 MW in California. LeBlanc adds, "We can't expect to capture all of this potential. But with the right product, pricing scheme, and marketing, it would be possible to help relieve price pressure on the whole system." The E SOURCE study, "Energy Pricing and Load Management: What Do End-Users Want?" can help energy service providers design appropriate products and identify the customer groups most likely to participate in such programs. It's based on an extensive research with over 700 energy decision-makers across the U.S. and Canada. For further information about "Energy Pricing and Load Management," please call Bill LeBlanc at 720-548-5476 or Tia Hensler, market research director, at 720-548-5614. SOURCE E SOURCE CONTACT: Bill LeBlanc, 720-548-5476, or Tia Hensler, market research director, 720-548-5614, both for E SOURCE Web site: http://www.esource.com ***********
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## 27 0 0 0 0 0 3 0 0
## 47 0 0 0 0 0 0 0 0
## 110 0 0 0 0 0 0 0 0
## 111 0 0 0 0 0 0 0 0
## 128 0 0 0 0 0 0 1 0
## 132 0 0 0 0 0 0 0 0
## 227 0 0 0 0 0 0 0 0
## 592 1 0 0 1 0 0 0 0
## 796 0 0 0 0 0 0 0 0
## street strong structur subject submit success sue suggest suit summari
## 7 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 1 0 0 0 0 0 0
## 27 0 0 0 0 0 0 0 0 0 0
## 47 0 0 0 0 0 0 0 0 0 0
## 110 0 0 0 1 0 0 0 0 2 0
## 111 0 0 0 1 0 0 0 0 0 0
## 128 0 0 0 1 0 0 0 0 0 0
## 132 0 0 0 2 0 0 0 0 0 0
## 227 0 0 1 0 0 0 0 0 0 1
## 592 0 0 0 2 0 0 0 1 0 0
## 796 0 0 0 1 0 0 0 0 1 0
## summer suppli supplier support sure susan system take taken talk tana
## 7 0 0 0 0 0 0 0 0 0 0 0
## 9 0 3 0 0 0 0 0 1 0 0 0
## 27 0 0 0 0 1 0 0 0 0 1 0
## 47 0 0 0 0 0 0 0 0 0 0 0
## 110 0 0 0 0 0 0 0 0 0 0 0
## 111 0 0 0 0 0 0 0 1 0 0 0
## 128 0 2 0 0 0 0 5 2 0 0 0
## 132 0 0 0 0 0 0 0 1 0 0 0
## 227 0 0 0 0 0 0 0 0 0 0 0
## 592 1 0 0 1 1 1 0 2 1 0 0
## 796 0 1 0 0 0 0 2 1 0 0 0
## tariff taylorhouectect team technolog tell term termin texa thank that
## 7 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0
## 27 0 0 0 0 0 0 0 0 0 0
## 47 0 0 0 0 0 2 0 0 0 0
## 110 0 0 0 0 0 0 0 0 1 0
## 111 0 0 0 0 0 0 0 0 1 0
## 128 0 0 0 0 0 0 0 0 0 0
## 132 0 0 0 0 0 0 0 0 0 0
## 227 0 0 0 0 0 0 0 0 0 0
## 592 0 0 0 0 0 0 0 0 0 0
## 796 0 0 0 0 0 0 1 0 0 0
## therefor thing think third though thought three thursday tim time
## 7 0 0 0 0 1 0 0 0 0 0
## 9 1 0 0 0 0 0 0 1 0 0
## 27 0 0 0 0 0 0 0 0 0 1
## 47 0 0 0 0 0 0 0 0 0 0
## 110 0 0 0 0 0 0 0 0 0 0
## 111 0 0 0 1 0 1 0 0 0 1
## 128 0 0 0 0 0 0 0 0 1 2
## 132 0 0 0 0 0 0 0 0 1 0
## 227 0 0 0 0 0 0 0 0 0 1
## 592 0 1 1 0 0 0 1 0 1 0
## 796 0 0 0 0 0 0 1 0 0 0
## today togeth told tom tomorrow top total trade trader transact
## 7 2 0 1 0 1 0 0 0 0 0
## 9 0 0 1 0 0 0 0 0 0 0
## 27 0 0 0 0 1 0 0 0 0 0
## 47 0 0 0 0 0 0 0 0 0 0
## 110 0 0 0 0 0 0 0 0 0 0
## 111 1 0 0 0 0 0 0 0 0 5
## 128 0 0 0 0 0 0 0 0 0 0
## 132 0 0 0 1 0 0 0 0 0 0
## 227 1 0 0 0 0 0 0 0 0 0
## 592 0 0 1 0 0 0 0 2 0 0
## 796 1 1 0 0 0 1 0 2 0 0
## transfer transmiss transport tri tuesday turn two type understand unit
## 7 0 0 0 0 0 0 0 0 0 0
## 9 0 0 1 0 0 0 0 0 0 0
## 27 0 0 0 1 0 0 0 0 0 2
## 47 0 0 3 0 0 0 0 0 0 0
## 110 1 1 0 0 0 0 0 0 2 0
## 111 0 0 0 0 0 0 0 0 8 0
## 128 0 4 0 0 0 0 0 0 0 0
## 132 0 0 1 0 0 0 0 0 0 0
## 227 0 0 0 1 0 0 0 0 0 0
## 592 0 0 1 0 0 0 1 0 0 0
## 796 0 0 0 0 0 0 5 0 0 0
## unless updat upon use util valu various version via view vinc visit
## 7 0 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 1 1 0 0 0 0 0 0 0 0
## 27 0 0 0 0 0 0 0 0 0 0 0 0
## 47 0 0 0 0 0 0 0 0 0 0 0 0
## 110 0 0 0 2 0 0 0 0 0 2 0 0
## 111 0 0 1 3 3 0 2 0 0 0 0 0
## 128 0 0 1 3 0 0 0 0 0 0 0 0
## 132 0 0 0 1 0 0 0 0 0 0 0 0
## 227 0 0 0 0 0 0 0 0 0 0 0 0
## 592 0 0 0 1 4 0 0 1 0 0 0 0
## 796 0 0 0 2 1 0 0 0 2 0 0 0
## volum want washington water way web websit wednesday week well west
## 7 0 1 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0 0
## 27 0 3 0 0 0 0 0 0 1 0 0
## 47 4 0 0 0 0 0 0 0 0 0 0
## 110 0 0 0 0 0 0 0 0 0 0 0
## 111 1 0 0 0 0 0 0 0 1 0 0
## 128 0 0 0 0 0 2 0 0 0 0 0
## 132 0 0 0 0 0 0 0 0 0 0 0
## 227 0 0 0 0 0 0 0 0 0 0 0
## 592 0 0 0 0 0 0 0 0 1 1 0
## 796 0 2 0 0 0 3 0 0 0 1 0
## whether wholesal will william within without word work year yesterday
## 7 0 0 3 0 0 0 0 0 0 0
## 9 0 0 2 1 0 0 0 0 0 0
## 27 0 1 3 0 0 0 0 1 0 0
## 47 0 0 1 0 0 0 0 0 0 0
## 110 0 0 6 0 0 0 0 0 0 0
## 111 0 1 3 0 0 1 0 1 0 0
## 128 0 0 5 0 0 1 0 1 3 0
## 132 0 0 0 0 0 0 0 0 0 0
## 227 0 0 1 0 0 0 0 1 0 0
## 592 2 1 0 1 0 0 0 1 0 1
## 796 1 2 1 0 0 0 0 0 1 0
## yet york responsive.fctr.predict.Final.rpart.prob
## 7 0 0 0.1042048
## 9 0 0 0.1042048
## 27 1 0 0.1042048
## 47 0 0 0.1042048
## 110 0 0 0.7843137
## 111 0 0 0.7843137
## 128 0 0 0.1042048
## 132 0 0 0.1042048
## 227 0 0 0.1042048
## 592 0 0 0.7843137
## 796 1 0 0.7843137
## responsive.fctr.predict.Final.rpart
## 7 N
## 9 N
## 27 N
## 47 N
## 110 Y
## 111 Y
## 128 N
## 132 N
## 227 N
## 592 Y
## 796 Y
## responsive.fctr.predict.Final.rpart.accurate .label
## 7 TRUE .7
## 9 FALSE .9
## 27 FALSE .27
## 47 FALSE .47
## 110 FALSE .110
## 111 FALSE .111
## 128 FALSE .128
## 132 FALSE .132
## 227 FALSE .227
## 592 TRUE .592
## 796 TRUE .796
tmp_replay_lst <- replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.new.prediction")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
## 6.0000 6 0 0 1 2
print(ggplot.petrinet(tmp_replay_lst[["pn"]]) + coord_flip())
Null Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## chunk_label chunk_step_major chunk_step_minor elapsed
## 10 fit.models 5 1 965.105
## 9 fit.models 5 0 474.244
## 11 fit.data.training.all 6 0 980.008
## 12 fit.data.training.all 6 1 994.875
## 7 select_features 4 0 22.648
## 13 predict.data.new 7 0 1001.017
## 6 extract_features 3 0 8.865
## 2 cleanse_data 2 0 3.001
## 8 remove_correlated_features 4 1 24.394
## 4 manage_missing_data 2 2 4.941
## 3 inspectORexplore.data 2 1 3.749
## 5 encodeORretype.data 2 3 5.265
## 1 import_data 1 0 0.002
## elapsed_diff
## 10 490.861
## 9 449.850
## 11 14.903
## 12 14.867
## 7 13.783
## 13 6.142
## 6 3.600
## 2 2.999
## 8 1.746
## 4 1.192
## 3 0.748
## 5 0.324
## 1 0.000## [1] "Total Elapsed Time: 1,001.017 secs"
## R version 3.1.3 (2015-03-09)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.3 (Yosemite)
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] tcltk grid stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] randomForest_4.6-10 rpart.plot_1.5.2 rpart_4.1-9
## [4] ROCR_1.0-7 gplots_2.16.0 caret_6.0-41
## [7] lattice_0.20-31 tm_0.6 NLP_0.1-6
## [10] sqldf_0.4-10 RSQLite_1.0.0 DBI_0.3.1
## [13] gsubfn_0.6-6 proto_0.3-10 reshape2_1.4.1
## [16] plyr_1.8.1 caTools_1.17.1 doBy_4.5-13
## [19] survival_2.38-1 ggplot2_1.0.1
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-6 BradleyTerry2_1.0-6 brglm_0.5-9
## [4] car_2.0-25 chron_2.3-45 class_7.3-12
## [7] codetools_0.2-11 colorspace_1.2-6 compiler_3.1.3
## [10] digest_0.6.8 e1071_1.6-4 evaluate_0.5.5
## [13] foreach_1.4.2 formatR_1.1 gdata_2.13.3
## [16] gtable_0.1.2 gtools_3.4.1 htmltools_0.2.6
## [19] iterators_1.0.7 KernSmooth_2.23-14 knitr_1.9
## [22] labeling_0.3 lme4_1.1-7 MASS_7.3-40
## [25] Matrix_1.2-0 mgcv_1.8-6 minqa_1.2.4
## [28] munsell_0.4.2 nlme_3.1-120 nloptr_1.0.4
## [31] nnet_7.3-9 parallel_3.1.3 pbkrtest_0.4-2
## [34] quantreg_5.11 RColorBrewer_1.1-2 Rcpp_0.11.5
## [37] rmarkdown_0.5.1 scales_0.2.4 slam_0.1-32
## [40] SparseM_1.6 splines_3.1.3 stringr_0.6.2
## [43] tools_3.1.3 yaml_2.1.13